Blockchain and AI in Shaping the Modern Education System
Kumar R., Kumar P., Sobin C.C., Subheesh N.P.
Book, Blockchain and AI in Shaping the Modern Education System, 2025, DOI Link
View abstract ⏷
In today’s rapidly evolving digital landscape, blockchain and artificial intelligence (AI) are at the forefront of transforming various industries, and education is no exception. The convergence of these two revolutionary technologies promises to reshape the modern education system by enhancing data security, promoting personalized learning, and creating decentralized frameworks for record-keeping and credentialing. This book delves into how blockchain and AI can drive a more inclusive, efficient, and secure educational ecosystem, where student-centered approaches and data integrity are paramount. This book is organized into several sections, each exploring the distinct roles of blockchain and AI within education. It begins with an introduction to the fundamental principles of these technologies and an overview of their individual strengths. Following this, chapters examine blockchain’s role in secure credential verification, decentralized learning platforms, and the protection of digital records. Next, the discussion shifts to AI applications, covering adaptive learning models, predictive analytics, and AI-driven administrative support. Finally, the book provides real-world case studies and future projections on how blockchain and AI together can tackle challenges in education, such as data privacy, resource distribution, and student engagement, ultimately creating an interconnected and resilient educational framework. This book is designed for educators, administrators, policymakers, technology enthusiasts, and researchers who are interested in the transformative potential of emerging technologies in education. It serves as a comprehensive guide for those looking to understand the practical applications and implications of blockchain and AI in the modern education system.
Enhancing Deep Boosting Algorithm Performance for Panic Disorder Detection through Biometric and Spatiotemporal Data Integration
Gabriel C.I.-O., Kumar R., Prasad K.
Article, IEEE Transactions on Artificial Intelligence, 2025, DOI Link
View abstract ⏷
With the increasing proliferation of mobile devices, sensors, and Internet of Things (IoT) technologies, intensive research continues in the scientific community, driven by a commitment to promoting the well-being of the general population. It is the need of the hour to pay more attention to mental health as it directly impacts people's lives. This paper aims to predict panic disorder 'PD' state by analyzing numerous health parameters in real time. To achieve this, the machine learning technique Bayesian-Light Gradient Boosting Machine hereafter referred to as 'B-LGBM' deep boosting is used to diagnose the health status of a person with PD. Hyperparameter optimization using the Bayesian technique is applied to identify the optimal set of parameters for the base learners, resulting in refined regularization values and reduced errors (overfitting) within the proposed model. Experiments on the open-source dataset reveal that the proposed B-LGBM model performed competitively with a mean square error 'MSE' score of 0.0364 and an accuracy of 96.36%. By combining artificial intelligence models with blockchain technology, future studies can enhance prediction accuracy and ensure secure, privacy-preserving assessment of complex physiological patterns in panic disorder. Our findings offer benefits in the areas of public mental health systems and clinical psychiatry in terms of tailored assessment and intervention.
Deep Digital Twin Services for Personalized MPX Treatment
Gabriel C.I.-O., Kumar R., Prasad K.
Conference paper, International Conference on Communication Systems and Networks, COMSNETS, 2025, DOI Link
View abstract ⏷
With the advent of smart healthcare services, rapid and automated diagnosis from images of skin lesions is critical to combat fast-spreading viruses such as monkeypox (Mpox or MPX) and significantly improve public health. The recent cases in Thailand reporting a suspected first case on August 21, 2024, and Sweden on August 14, 2024, among others, highlight the pandemic threat of Mpox. This study presents the Deep Digital Twin Services for Personalized Treatment (D2T-PT) model, which combines transfer learning and Digital Twin (DT) technology to improve the accuracy of Mpox detection and real-time monitoring, supported by the Squeeze-and-Excitation Block (SEB) attention mechanism, which opens up new horizons for personalized healthcare. Convolutional Neural Network (CNN) models were tested on the Monkeypox Skin Lesion Dataset (MSLD), with the advanced adaptive NasNetMobile model achieving excellent results: 100% recall, 98% ROC score, 97.78% accuracy with precision of 95%. This robust model enables physicians to make early and accurate Mpox diagnoses and monitor patient response to treatment in real-time, ultimately helping to contain the spread of the virus.
Secured Smart Water Resource Management: An Incentive-Based Blockchain Use Case
Peddibhotla U.S., C C S., Kumar R.
Article, ACS ES and T Water, 2025, DOI Link
View abstract ⏷
Water usage serves as a key factor in boosting groundwater levels, alleviating water scarcity, and enhancing sustainable farming practices. Yet, ineffective oversight and accountability among stakeholders often undermine water resource management (WRM). To address this issue, we introduce a framework called S2WRM, which integrates a dual strategy: a game theory-based method and a blockchain-based system. In the game theory component, we apply a Stackelberg game, positioning the Water Resource Management Authority (WRMA) as the leader and industrialists and farmers as followers. Through this sequential game, dynamic pricing is determined as the leader establishes pricing strategies across various scenarios, analyzing usage trends to boost WRMA revenue. In addition, a blockchain incentive system is implemented, using ERC-20 contracts to mint and distribute tokens as rewards. This blockchain framework is built on the Ethereum platform, with Stackelberg simulations coded in Python. Analysis of different scenarios reveals key insights: first, blockchain-based incentives improve consumption oversight, fostering transparency in stakeholder supply chains second, WRMA revenue rises as blockchain reduces overhead costs tied to dynamic pricing and third, sustainability is advanced through token limits and penalties.
Securing Agricultural Communications: Blockchain Integration in UAV Networks for Smart Farming
Peddibhotla U., Kumar R., Sobin C.C., Kumar P., Javeed D., Islam N.
Conference paper, 2024 IEEE International Conference on Communications Workshops, ICC Workshops 2024, 2024, DOI Link
View abstract ⏷
The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience.
Fostering Basic Electronics Teaching Competencies: Impact of the School Teachers’ Electronics Practicals Upskilling Program (STEP-UP)
Subheesh N.P., Rajeev A., Abhinav R., Mohandas H., Sobin C.C., Kumar P., Kumar R.
Conference paper, IEEE Global Engineering Education Conference, EDUCON, 2024, DOI Link
View abstract ⏷
School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as 'School Teachers' Electronics Practicals Upskilling Program (STEP-UP),' it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.
Secure Data Dissemination Scheme for Digital Twin Empowered Vehicular Networks in Open RAN
Kumar R., Kumar P., Aljuhani A., Jolfaei A., Islam A.K.M.N., Mohammad N.
Article, IEEE Transactions on Vehicular Technology, 2024, DOI Link
View abstract ⏷
The use of Open Radio Access Networks (Open RAN) in vehicular networks can lead to better connectivity, reliability, and performance. However, communication in this setting is often done over an unsecured wireless network, which creates a challenge in verifying the validity of received transactions by Internet of Vehicles (IoV) due to the untrusted network. It also creates a potential for attackers to tamper with the data content and conduct different IoV-related attacks. To address these issues, a new framework named 'STIoV' has been proposed for secure and trustworthy communication in IoV. The framework includes a mutual authentication scheme to register and exchange session keys among the IoV participants, and a credit-based trust management system to assign reputation scores for the vehicular devices. The latter scheme discards transactions with low credit scores. To overcome the complexity and variability of the IoV network, digital twin technology is used to map Road Side Units (RSU) servers into virtual space, which facilitates constructing the vehicular relation model. An Intrusion Detection System (IDS) based on deep learning techniques is also introduced to detect anomalies in the traffic flow. The legitimate data is further used by the blockchain scheme for transaction verification, block creation and addition. Finally, the proposed framework has been evaluated based on two network intrusion datasets, and the results show the accuracy and efficacy of STIoV in comparison to several recent state-of-the-art solutions.
An Intelligent and Interpretable Intrusion Detection System for Unmanned Aerial Vehicles
Javeed D., Gao T., Kumar P., Shoukat S., Ahmad I., Kumar R.
Conference paper, IEEE International Conference on Communications, 2024, DOI Link
View abstract ⏷
The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.
System for Emotion and Engagement Recognition in Education (SEERE): An AI-Enabled System for Responsive Teaching
Subheesh N.P., Vishnumolakala S.K., Vallamkonda S., Sobin C.C., Kumar P., Kumar R.
Conference paper, Proceedings - Frontiers in Education Conference, FIE, 2024, DOI Link
View abstract ⏷
This paper presents the System for Emotion and Engagement Recognition in Education (SEERE), a cutting-edge advancement integrating computer vision and deep learning tech-nologies to evaluate real-time student engagement through facial emotion recognition and eye tracking. SEERE, a transformative educational tool built on the robust YOLO V8 architecture, customizes the FER2013 dataset, making use of meticulously annotated emotion and eye position data. It goes further, es-tablishing a unique 'concentration metric,' a quantitative index of student engagement, bridging a gap in modern responsive teaching approaches. Higher concentration metrics signal height-ened student engagement, offering educators real-time data to adjust teaching techniques and feedback accordingly. The paper provides a thorough review of facial emotion recognition models, setting the stage for understanding the innovative strides made by SEERE. Detailed discussions on the prototype's design and architecture are followed by initial experimental results, reinforcing the system's validity and potential.
AI-Based Research Companion (ARC): An Innovative Tool for Fostering Research Activities in Undergraduate Engineering Education
Vishnumolakala S.K., Sobin C.C., Subheesh N.P., Kumar P., Kumar R.
Conference paper, IEEE Global Engineering Education Conference, EDUCON, 2024, DOI Link
View abstract ⏷
The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called 'AI-based Research Companion (ARC)' has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.
Data security enhancement in internet of things using optimised hashing algorithm
Arun Kumar U., Prem Kumar R., Siva Kumar S.A., Maruthi Shankar B., Mahendran G.
Article, International Journal of Ad Hoc and Ubiquitous Computing, 2024, DOI Link
View abstract ⏷
The internet of things (IoT) has advanced quickly, providing customers with significant convenience in a variety of areas, including smart homes, smart transportation, and more. It might potentially pose security issues too. Significant security difficulties for the communications between such devices are brought on by the development of smart devices in IoT networks. As the IoT ecosystems develop, blockchain will become a platform for their security. Blockchain is a decentralised, distributed technology that may be able to address the IoT network security issues. Blockchain can address IoT restrictions on privacy and data protection. IoT is not a good fit for blockchain because of its high computing complexity, limited scalability, significant bandwidth overhead, and latency. This study develops an effective blockchain paradigm to address IoT requirements. Initially, the dataset is collected from the IoT sensors and pre-processed using the normalisation method. The pre-processed data is validated using smart contracts and stored in the blockchain network. Proof of work (PoW) consensus protocol is employed for the validation of the blocks. We propose an optimal key search fuzzy hashing algorithm (OKSFHA) for enhancing the security of the data. To optimise the security enhancement Spider monkey optimisation (SMO) is employed. The proposed algorithm is compared with traditional algorithms to prove the efficiency of the suggested system.
Enhanced Supply Chain Management in Indian Agriculture Using SSI and Blockchain Leveraged by Digital Wallet
Peddibhotla U., Chandran S.C., Kumar P., Kumar R.
Conference paper, 2024 16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024, 2024, DOI Link
View abstract ⏷
The proliferation of agricultural supply chain encompasses participants such as farmers, intermediate silos, transformation plants, and clients. Managing this agro-supply chain involves various functions related to the flow of both materials and information. Before entering the market, crop protection products and inputs undergo rigorous testing and regulatory scrutiny. Despite these measures, counterfeit products reach end-users due to insufficient transparency and the sharing of outdated information among stakeholders. To address this issue, the paper suggests a three-tiered integrated solution: the Product layer, Blockchain layer, and SSI layer. This strategy involves attaching Near-Field Communication (NFC) tags to the packages at the product layer, with the blockchain monitoring each step of the supply chain. The NFC tags can be read to verify the authenticity and other details of the product. Certifications for products, inputs, and the identities of dealers and consumers are stored as self-sovereign IDs in digital wallets. The authenticity details of producers undergo auditing by the certification authority, which is then transferred to the verification authority. The Verifier confirms these details and generates a verifiable presentation received by the consumer, enabling them to make informed purchases. This approach eliminates product tampering and the involvement of unverified producers and dealers in the supply chain. The comprehensive explanation and investigation of proposed framework state adequate guidelines to make counterfeit resistance agricultural supply chain system.
An Automated Threat Intelligence Framework for Vehicle-Road Cooperation Systems
Kumar P., Kumar R., Jolfaei A., Mohammad N.
Article, IEEE Internet of Things Journal, 2024, DOI Link
View abstract ⏷
Vehicle-road cooperation systems (VRCSs) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Internet of Vehicles (IoV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. At the same time, cyber attacks have become more complex, persistent, organized, and weaponized in IoV network. Threat intelligence (TI) has emerged as a prominent security approach to obtain a complete view of the dynamically growing cyber threat environment. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs require a large investment of resources and use handcrafted features, making the entire process error-prone and time-consuming. To tackle these challenges, this article presents TIMIF, a deep-learning-based TI modeling and identification framework for intelligent IoV and is based on three key modules: first, the proposed TIMIF adopts an automated pattern extractor (APE) module to extract hidden patterns from IoV networks. Employing its output, we design a TI-based detection (TIBD) module to detect abnormal behavior and TI-attack type identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources, namely, HCRL-car hacking, ToN-IoT, and CICIDS-2017 to illustrate the utility of the TIMIF framework over some commonly used baselines and state-of-the-art techniques.
Convergence of IoT and Blockchain Ecosystem to Ensure Traceability and Reliability in Agricultural Supply Chain
Nair A., Peddibhotla U., Chandran S.C., Kumar R.
Conference paper, 2024 16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024, 2024, DOI Link
View abstract ⏷
The profound growth in the human population over the past two centuries has created a new issue in food security. Due to the high demand for food, there is an increasing burden on the agricultural supply chain (ASC) to satisfy the hunger of every individual. As a result, there is a possibility for spoilt or contaminated food to enter the ASC. If the end-consumer consumes these bad food products, it can lead to food poisoning and even death in certain circumstances. In order to ensure that the food delivered to the consumer is safe, it is necessary to monitor the food product as it passes through the different entities present in the ASC. The traditional ASC lacks traceability and reliability. Traceability is necessary in determining the origin of a crop, while reliability is necessary in preventing foul play by any entity. Therefore, developing a traceable and reliable system for the existing ASC model has become very important. The transparent, decentralized, and immutable qualities of Blockchain, along with the help from IoT devices, will allow us to actively trace the food from farm-to-fork as it passes through the supply chain while maintaining a high reliability between each entity. Thus, this paper proposes a novel ASC model, incorporated using Blockchain and IoT technology, to mitigate the traceability and reliability issues in the ASC.
Message from the ESPTA 2024 Workshop Chairs; MASS 2024
Kumar P., Kumar R., Sobin C.C.
Editorial, Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024, 2024, DOI Link
A Comprehensive Review on Energy Conservation in Demand Side Management
Kumar R.P., Mohanraj C., Karthikeyan R., Prameeth J.
Conference paper, 2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024 - Proceedings, 2024, DOI Link
View abstract ⏷
The primary research goal of the study is to build a management system for energy resources, particularly in micro-grids, which is utilized to improve energy consumption technique and management using renewable energy resources in order to produce a system that is affordable. Current developments in power grid and micro-grid technology include concepts for energy congestion and smart grid distributed generation. Due to the fact that these qualities are particularly helpful in increasing energy efficiency and lowering costs associated with new power plants, etc. In accordance with shifting load to offpeak periods from peak periods, demand side management will offer a solution for energy conservation that is both practical and affordable. And that in-turn reducing the many environmental impacts which is caused by using the conventional power resources which is used in smart grids helps in achieving the economic sustainability and addressing the effective energy demand are the main critical characteristics of demand side management systems. The various implementations for the demands like management and that algorithm safe for the future use for the monitoring purpose. By this search it is been analyzed that the production in the network congestion, improvement in power factor and average speed ratio with ultimately increases the unbalance in the grid.
HydroDrone: Multi-Drone Network for Secure Task Management in Smart Water Resource Management
Peddibhotla U.S., Kumar R., Sobin C.C.
Conference paper, Proceedings - 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2024, 2024, DOI Link
View abstract ⏷
Drones, also known as Unmanned Aerial vehicles(UAVs), are increasingly used in various applications, including agriculture, construction and infrastructure, environmental conservation, water resources management(WRM) etc. Multiple drones are interconnected with each other as UAV swarms. These UAV swarms offer a versatile and cost-effective tool for WRM. This paper proposes the multi-drone network for WRM known as HydroDrone. The purpose of HydroDrone is to provide valuable data and insights to support decision making and improve the resilience of water supply systems on the verge of ever-changing environmental conditions. In addition, we discussed task allocation to individual drones within the swarm. HydroDrone schedules and balances the task load for water resource management. We also introduced security to multi-drone communication with the base and core stations by incorporating blockchain technology due to its decentralized nature.
Blockchain and Digital Twin Enabled IoT Networks: Privacy and Security Perspectives
Kumar R., Kumar P., Sobin C.C.
Book, Blockchain and Digital Twin Enabled IoT Networks: Privacy and Security Perspectives, 2024, DOI Link
View abstract ⏷
This book reviews research works in recent trends in blockchain, AI, and Digital Twin based IoT data analytics approaches for providing the privacy and security solutions for Fog-enabled IoT networks. Due to the large number of deployments of IoT devices, an IoT is the main source of data and a very high volume of sensing data is generated by IoT systems such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT systems is a fundamental research issue. For the deployment of the Fog-enabled-IoT system in different applications such as healthcare systems, smart cities and smart grid systems, security, and privacy of big IoT data and IoT networks are key issues. The current centralized IoT architecture is heavily restricted with various challenges such as single points of failure, data privacy, security, robustness, etc. This book emphasizes and facilitates a greater understanding of various security and privacy approaches using the advances in Digital Twin and Blockchain for data analysis using machine/deep learning, federated learning, edge computing and the countermeasures to overcome these vulnerabilities.
IMPLEMENTATION OF LOAD MANAGEMENT SYSTEM FOR A GRID CONNECTED HYBRID ENERGY SYSTEM COUPLED WITH BATTERY FOR HOSPITAL LOAD UNDER SUSTAINABLE DEVELOPMENT
Article, Journal of Environmental Protection and Ecology, 2024,
View abstract ⏷
This article deals with load management system for a grid connected hybrid energy system coupled with battery for hospital load. In the proposed system, solar, wind, battery and grid are sources of energy proposed to supply hospital load. To enrich the energy efficiency of a proposed power system, Genetic algorithm (GA) is proposed in the scheduling operation. Transmitting ratio is the parameter optimised by GA to enrich the cost benefit and managing load demand. Time of Day pricing is a real time pricing system; it has turned out to be a successful strategy for lowering peak electricity demand across the board, but especially in developed nations. Using MATLAB, the proposed performance of GA in the aspect of daily cost benefit with the comparative analysis of conventional load management system. In this article analysis of LMS is validated with the experimental setup of 4 kW using Delta HMI and Embedded system with four sources and three loads. In this analysis Load management system is implemented using Delta HMI.
Blockchain-Based Authentication and Explainable AI for Securing Consumer IoT Applications
Kumar R., Javeed D., Aljuhani A., Jolfaei A., Kumar P., Islam A.K.M.N.
Article, IEEE Transactions on Consumer Electronics, 2024, DOI Link
View abstract ⏷
The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.
Explainable AI and Blockchain for Metaverse: A Security and Privacy Perspective
Kumar P., Kumar R., Aloqaily M., Islam A.K.M.N.
Article, IEEE Consumer Electronics Magazine, 2024, DOI Link
View abstract ⏷
The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3-D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this article, we first discuss the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of the proposed architecture to tackle the aforementioned obstacles. Last, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.
Digital Twins-enabled Zero Touch Network: A smart contract and explainable AI integrated cybersecurity framework
Kumar R., Aljuhani A., Javeed D., Kumar P., Islam S., Islam A.K.M.N.
Article, Future Generation Computer Systems, 2024, DOI Link
View abstract ⏷
Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques.
Blockchain and explainable AI for enhanced decision making in cyber threat detection
Kumar P., Javeed D., Kumar R., Islam A.K.M.N.
Article, Software - Practice and Experience, 2024, DOI Link
View abstract ⏷
Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI-based threat hunting is data-driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat-hunting capability. In this paper, we present a blockchain-enabled eXplainable AI (XAI) for enhancing the decision-making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof-of-Authority (C-PoA) consensus. Second, a novel deep learning-based threat-hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi-head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.
An Integrated Framework for Enhancing Security and Privacy in IoT-Based Business Intelligence Applications
Kumar R., Kumar P., Jolfaei A., Islam A.K.M.N.
Conference paper, Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2023, DOI Link
View abstract ⏷
Business intelligence (BI) is the procedure of strategically planning and using a variety of tools and techniques to get important data insights and make wise business decisions. The Internet of Things (IoT) has emerged as the main source of big data with the most real-time data used across all business sectors in recent years. However, the expansion of business results is impacted by the integration of IoT as data sources with conventional BI systems. This is because there are now more security and privacy concerns in IoT ecosystems as a result of adversaries undertaking data inference and poisoning attacks on networked IoT devices via the open communication medium Internet. This study proposes an integrated architecture for strengthening security and privacy in IoT-based BI applications, which is inspired by the discussion above. The suggested structure contains two engines: an intrusion detection engine and a two-level privacy engine. Due to adversaries conducting data inference and poisoning attacks on networked IoT devices over the open communication medium Internet, there are now additional security and privacy risks in IoT ecosystems. Based on the discussion above, this study suggests an integrated architecture for enhancing security and privacy in IoT-based BI applications. The two different engines are designed namely two-level privacy and intrusion detection engine. The experimental outcomes utilising the real IoT-based dataset ToN-IoT show that the suggested strategy outperforms previous peer privacy-preserving machine learning algorithms in terms of detection rate, accuracy, F1 score, and precision.
Deep-Learning-Based Blockchain for Secure Zero Touch Networks
Kumar R., Kumar P., Aloqaily M., Aljuhani A.
Article, IEEE Communications Magazine, 2023, DOI Link
View abstract ⏷
The recent technological advancements in wireless communication systems and the Internet of Things (IoT) have accelerated the development of zero touch networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated service-level policies that cannot be fulfilled by the traditional network management and orchestration approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep-learning-and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel variational autoencoder (VAE) and attention-based gated recurrent units (AGRU)-based intrusion detection system (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, smart contracts (SCs), elliptic curve cryptography (ECC), and a proof of authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.
A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system
Kumar P., Kumar R., Gupta G.P., Tripathi R., Jolfaei A., Najmul Islam A.K.M.
Article, Journal of Parallel and Distributed Computing, 2023, DOI Link
View abstract ⏷
The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.
Fog Intelligence for Secure Smart Villages: Architecture and Future Challenges
Aljuhani A., Kumar P., Islam A.K.M.N., Kumar R., Jolfaei A.
Article, IEEE Consumer Electronics Magazine, 2023, DOI Link
View abstract ⏷
The Internet of Things (IoT) technology is considered the foundation for next-generation smart villages due to its ability to use sustainable information and communication technologies. The smart villages can enable real-time data analytics and can automate decision-making for local villagers in terms of agriculture, health care, transportation, environment, and energy. However, most of the wireless sensing devices exchange information using public networks and therefore may not be able to resist all forms of attacks. In addition, most of the IoT devices are resource restricted and use cloud servers to process and store data. However, when IoT devices communicate with cloud computing data centers, the volume of data causes network congestion. To provide efficient and secure services, a new network architecture named distributed fog computing (DFC) can be created and integrated with the IoT-based smart villages deployment. Motivated from the aforementioned discussions, this article explores the integration of DFC with IoT in improving security and privacy solutions for consumer electronic devices used by villages. As a case study, we also design and evaluate the performance of an intrusion detection system in a DFC-based smart village environment. Finally, we discuss several open security issues and challenges regarding Fog-to-Things enabled smart villages.
Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity
Kumar P., Kumar R., Aljuhani A., Javeed D., Jolfaei A., Islam A.K.M.N.
Article, Solar Energy, 2023, DOI Link
View abstract ⏷
Internet of Things (IoT)-enabled Smart Grid (SG) network is envisioned as the next-generation network for intelligent and efficient electric power transmission. In SG environment, the Smart Meters (SMs) mostly exchange services and data from Service Providers (SPs) via insecure public channel. This makes the entire SG ecosystem vulnerable to various security threats. Motivated from the aforementioned challenges, we incorporate Digital Twin (DT) technology, Software-Defined Networking (SDN), Deep Learning (DL) and blockchain into the design of a novel SG network. Specifically, a secure communication channel is first designed using an authentication method based on blockchain technology that has the ability to withstand a number of well-known assaults. Second, a new DL architecture that includes a self-attention mechanism, a Bidirectional-Gated Recurrent Unit (Bi-GRU) model, fully connected layers, and a softmax classifier is designed to enhance the attack detection process in SG environments. To deliver low latency and real-time services, the SDN is next employed as the network's backbone to send requests from SMs to a global SDN controller. DT technology is finally integrated into the SDN control plane, which stores the operating states and behavior models of SMs and communicates with SMs. The efficiency of the proposed framework is demonstrated by the blockchain implementation used in the SG network to assess computing time for the various numbers of transactions per block. Finally, the numerical results based on the N-BaIoT dataset shows better intrusion detection.
A Secure Data Dissemination Scheme for IoT-Based e-Health Systems using AI and Blockchain
Kumar P., Kumar R., Garg S., Kaur K., Zhang Y., Guizani M.
Conference paper, Proceedings - IEEE Global Communications Conference, GLOBECOM, 2022, DOI Link
View abstract ⏷
In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.
Blockchain and Deep Learning for Cyber Threat-Hunting in Software-Defined Industrial IoT
Kumar R., Kumar P., Kumar A., Franklin A.A., Jolfaei A.
Conference paper, 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, 2022, DOI Link
View abstract ⏷
The softwarized infrastructure of Software-Defined Industrial Internet of Things (SDIIoT) offers a cost-effective solution to improve flexibility and reliability in network management but faces several critical challenges. First, th Majority of SDIIoT entities operate over wireless channel, which expose them to a variety of attacks (e.g., man-in-the-middle, replay, and impersonation attacks) and also the centralized nature of SDN controller is prone to single point attacks. Second, network traffic in the SDIIoT is associated with large scale, high dimension and redundant data, all of which present significant hurdles in the development of efficient flow analyzer. In this regard, we present a novel blockchain and Deep Learning (DL) integrated framework for protecting confidential information and hunting cyber threats against SDIIoT and their network traffic. First the blockchain module is proposed to securely transmit industrial data from IIoT sensors to controllers of SDN via forwarding nodes (i.e., OpenFLow switches) using Clique Proof-of-Authority (C-PoA) consensus mechanism. A novel flow analyzer based on DL architecture named LSTMSCAE-AGRU is designed by combining Long Short-Term Memory Stacked Contractive AutoEncoder (LSTMSCAE) with Attention-based Gated Recurrent Unit (AGRU) at the control plane. The latter first extracts low-dimensional features in an unsupervised manner, which is then fed to AGRU for hunting anomalous switch requests. The proposed framework can withstand a variety of well-known cyber threats and mitigate the single point of controller failure problem in SDIIoT.
Blockchain and Deep Learning Empowered Secure Data Sharing Framework for Softwarized UAVs
Kumar P., Kumar R., Kumar A., Franklin A.A., Jolfaei A.
Conference paper, 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, 2022, DOI Link
View abstract ⏷
Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from the data layer via OpenFlow protocols. The softwarized UAV enable ubiquitous connection, as well as a flexible, cost-effective, and improved method for upgrading all network services without shutting down the entire system. However, the connectivity of UAVs with OpenFlow switches and their heavy reliance on unsecured communication protocols makes the entire network vulnerable. This is a critical concern, particularly in combat surveillance, where eavesdropping, adding, changing, or deleting messages during communications between deployed UAVs and SDN controller is a possible threat. To mitigate the aforementioned issues, this paper presents a novel secure data sharing framework for softwarized UAV environments that incorporates blockchain and Deep Learning (DL). First we present a blockchain-based technique to reg-ister, verify and thereafter validate the communication entities in softwarized UAV environment using smart contract-based Proof-of-Authentication (PoA) consensus mechanism. Additionally, a new deep neural network architecture-based flow analyzer is designed to detect illegitimate transactions. The latter combines a Stacked Contractive Sparse AutoEncoder with Attention-based Long Short-term Memory Neural Network (SCSAE-ALSTM) to improve intrusion detection process. The effectiveness of our framework over several standard baseline methodologies is demonstrated by security analysis and experimental findings.
A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network
Kumar R., Kumar P., Tripathi R., Gupta G.P., Garg S., Hassan M.M.
Article, Journal of Parallel and Distributed Computing, 2022, DOI Link
View abstract ⏷
The Internet of Things (IoT) is emerging as a new technology for the development of various critical applications. However, these applications are still working on centralized storage architecture and have various key challenges like privacy, security, and single point of failure. Recently, the blockchain technology has emerged as a backbone for the IoT-based application development. The blockchain can be leveraged to solve privacy, security, and single point of failure (third-part dependency) issues of IoT applications. The integration of blockchain with IoT can benefit both individual and society. However, 2017 Distributed Denial of Service (DDoS) attack on mining pool exposed the critical fault-lines among blockchain-enabled IoT network. Moreover, this application generates huge amount of data. Machine Learning (ML) gives complete autonomy in big data analysis, capabilities of decision making and therefore is used as an analytical tool. Thus, in order to address above challenges, this paper proposes a novel distributed Intrusion Detection System (IDS) using fog computing to detect DDoS attacks against mining pool in blockchain-enabled IoT Network. The performance is evaluated by training Random Forest (RF) and an optimized gradient tree boosting system (XGBoost) on distributed fog nodes. The proposed model effectiveness is assessed using an actual IoT-based dataset i.e., BoT-IoT, which includes most recent attacks found in blockchain-enabled IoT network. The results indicate, for binary attack-detection XGBoost outperforms whereas for multi-attack detection Random Forest outperforms. Overall on distributed fog nodes RF takes less time for training and testing compared to XGBoost.
A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System
Kumar R., Kumar P., Tripathi R., Gupta G.P., Kumar N., Hassan M.M.
Article, IEEE Transactions on Intelligent Transportation Systems, 2022, DOI Link
View abstract ⏷
Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs-RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.
P2TIF: A Blockchain and Deep Learning Framework for Privacy-Preserved Threat Intelligence in Industrial IoT
Kumar P., Kumar R., Gupta G.P., Tripathi R., Srivastava G.
Article, IEEE Transactions on Industrial Informatics, 2022, DOI Link
View abstract ⏷
The industrial Internet of Things (IIoT) is a fast-growing network of Internet-connected sensing and actuating devices aimed to enhance manufacturing and industrial operations. This interconnection generates a high volume of data over the IIoT network and raises serious security (e.g., the rapid evolution of hacking techniques), privacy (e.g., adversaries performing data poisoning and inference attacks), and scalability issues. To mitigate the aforementioned challenges, this article presents, a new privacy-preserved threat intelligence framework (P2TIF) to protect confidential information and to identify cyber-threats in IIoT environments. There are two major elements in the proposed P2TIF framework. First, a scalable blockchain module that enables secure communication of IIoT data and prevents data poisoning attacks. Second, a deep learning module that transforms actual data into a new format and protects data from inference attacks using a deep variational autoencoder (DVAE) technique. The encoded data are then employed by a threat detection system using attention-based deep gated recurrent neural network (A-DGRNN) to recognize malicious patterns in IIoT environments. The proposed framework is validated using two different network data sources, i.e., ToN-IoT and IoT-Botnet. Security analysis and experimental results revealed the high efficiency and scalability of the proposed P2TIF framework.
BDEdge: Blockchain and Deep-Learning for Secure Edge-Envisioned Green CAVs
Kumar P., Kumar R., Gupta G.P., Tripathi R.
Article, IEEE Transactions on Green Communications and Networking, 2022, DOI Link
View abstract ⏷
Green Connected and Autonomous Vehicles (CAVs) are the future of next-generation Intelligent Transportation Systems (ITS) that will help humans to improve road safety and reduce pollution, energy consumption, and travel delays. To increase the performance of green CAV, the data generated by Autonomous Vehicles (AVs) and associated infrastructure needs to be processed in real-time. Mobile Edge Computing (MEC) is a promising paradigm that can be integrated with green CAV to save energy and to improve the network performance in terms of low latency for data processing. However, MEC servers and other communication entities in green CAV environment cannot be fully trusted and may bring vulnerabilities related to data privacy and security. Motivated by the above challenges, we design a blockchain and Deep-Learning (DL)-enabled secure data processing framework for an edge-envisioned green CAV environment (hereafter referred to as BDEdge). In blockchain-based scheme, all communication entities are registered, verified and thereafter validated using smart contract-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authenticated data is forwarded to DL scheme. In DL-based scheme, an Intrusion Detection System (IDS) based on a hybrid model of Sparse Auto-Encoder-enabled Attention Bidirectional Gated Recurrent Unit (SAE-ABIGRU) is designed by analyzing the link load behaviors of the MEC-enabled Road Side Unit (RSU) server. The security analysis and comparative simulation findings shows that the proposed BDEdge framework can significantly reduce false alarm rate and increase accuracy close to 99%.
BDTwin: An Integrated Framework for Enhancing Security and Privacy in Cybertwin-Driven Automotive Industrial Internet of Things
Kumar R., Kumar P., Tripathi R., Gupta G.P., Garg S., Hassan M.M.
Article, IEEE Internet of Things Journal, 2022, DOI Link
View abstract ⏷
The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users' digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.
Building an IPFS and blockchain-based decentralized storage model for medical imaging
Book chapter, Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 2022, DOI Link
View abstract ⏷
Currently, sharing and access of medical imaging is a significant element of present healthcare systems, but the existing infrastructure of medical image sharing depends on third-party approval. In this chapter, the authors have proposed a framework in order to provide a decentralized storage model for medical image sharing through IPFS and blockchain technology that remove the hurdle of third-party dependency. In the proposed model, the authors are sharing the imaging and communications in medicine (DICOM) medical images, which consist of various information related to disease, and hence, the framework can be utilized in the real-time application of the healthcare system. Moreover, the framework maintains the feature of immutability, privacy, and availability of information owing to the blockchain-based decentralized storage model. Furthermore, the authors have also discussed how the information can be accessed by the peers in the blockchain network with the help of consensus. To implement the framework, they have used the python ask and anaconda python.
Blockchain-enabled secure communication for unmanned aerial vehicle (UAV) networks
Kumar R., Aljuhani A., Kumar P., Kumar A., Franklin A., Jolfaei A.
Conference paper, DroneCom 2022 - Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, 2022, DOI Link
View abstract ⏷
While 5G can provide high-speed Internet connectivity and over-the-horizon control for Unmanned Aerial Vehicles (UAVs), authentication becomes a key security component in 5G-enabled UAVs. This is due to fact that the communicating entities in the network mostly uses unsecured communication channel to exchange critical surveillance data. Authentication thus plays a crucial role in the 5G-enabled UAV network, providing a range of security services such as credential privacy, Session-Key (SK) security, and secure mutual authentication. However, transparency, anonymity, traceability and centralized control are few major security requirements that cannot be fulfilled by the traditional authentication schemes. One of the upcoming technologies that can provide a solution for present centralized 5G-enabled UAV network is blockchain-based authentication scheme. Motivated from aforementioned discussion, this paper presents a Permissioned Blockchain empowered Secure Authentication and Key Agreement framework in 5G-enabled UAVs. In this framework, first an authentication phase between UAV-to-UAV, UAV-to-Edge Server (ES) and Edge-to-Cloud Server (CS) supporting mutual authentication and key agreement is proposed. The authenticated surveillance data collected from UAV is used by the peer-to-peer CS for transaction verification, block creation and addition using smart contract-based consensus mechanism. The practical implementation of framework shows the effectiveness of the proposed approach.
Permissioned Blockchain and Deep Learning for Secure and Efficient Data Sharing in Industrial Healthcare Systems
Kumar R., Kumar P., Tripathi R., Gupta G.P., Islam A.K.M.N., Shorfuzzaman M.
Article, IEEE Transactions on Industrial Informatics, 2022, DOI Link
View abstract ⏷
The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.
P2SF-IoV: A Privacy-Preservation-Based Secured Framework for Internet of Vehicles
Kumar R., Kumar P., Tripathi R., Gupta G.P., Kumar N.
Article, IEEE Transactions on Intelligent Transportation Systems, 2022, DOI Link
View abstract ⏷
With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.
SMDSB: Efficient Off-Chain Storage Model for Data Sharing in Blockchain Environment
Kumar R., Marchang N., Tripathi R.
Conference paper, Advances in Intelligent Systems and Computing, 2021, DOI Link
View abstract ⏷
Blockchain technology has been gaining great attention in recent years. Owing to its feature of immutability, the data volume of the blockchain network is continuously growing in size. As of now, the size of Bitcoin distributed ledger has reached about 200 GB. Consequently, the increasing size of the blockchain network ledger prevents many peers from joining the network. Hence, the growing size not only limits the expansion of network but also the development of the blockchain ledger. This calls for development of efficient mechanisms for storage and bandwidth synchronization. As a step toward this goal, we propose an IPFS-based decentralized off-chain storage called storage model for data sharing in blockchain (SMDSB), to store the data volume of the blockchain network. In the proposed model, the miners validate and deposit the transactions into an IPFS-based decentralized storage, whereas they store the hash of the transactions in a blockchain network. Thus, by utilizing the characteristics of IPFS-based off-chain storage and its feature of hash creation, the blockchain ledger size can be significantly reduced. Implementation of SMDSB results in reduction of Bitcoin storage by 81.54%, Ethereum by 53.86%, and Hyperledger by 62.59%. Additionally, the experimental results also highlight the impact on storage cost for 1 transaction per second (TPS) in a year.
Scalable and secure access control policy for healthcare system using blockchain and enhanced Bell–LaPadula model
Article, Journal of Ambient Intelligence and Humanized Computing, 2021, DOI Link
View abstract ⏷
Access control is a policy in data security that controls access to resources. The current access control mechanisms are facing many problems, due to the interference of the third-party, privacy, and security of data. These problems can be addressed by blockchain, the technology that gained major attention in recent years and has many capabilities. However, in the blockchain network, every peer maintains the same state of the ledger to view the complete history of transactions that leads to scalability issues in the blockchain network. To address the problem of scalability we propose an enhanced Bell–LaPadula model and categorized the peers and transactions in different clearance and security levels. The peers don’t have to maintain the complete history of transactions owing to the clearance level. To provide data security in the network we constructed a dynamic access control policies using a smart contracts. We test our model on a blockchain-based healthcare network. The Hyperledger Fabric tool is used to run a complete infrastructure of healthcare organization while the Hyperledger composer modeling tool is used to implement the smart contracts and to provide dynamic access control functionality on the blockchain network.
SP2F: A secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles
Kumar R., Kumar P., Tripathi R., Gupta G.P., Gadekallu T.R., Srivastava G.
Article, Computer Networks, 2021, DOI Link
View abstract ⏷
The current advancement in Unmanned Aerial Vehicles (UAVs) and the proliferation of the Internet of Things (IoT) devices is revolutionizing conventional farming operations into precision agriculture. The agricultural UAVs combined with IoT use an open channel i.e., the Internet to assist cultivators with data collection, processing, monitoring, and making correct decisions on the farm. However, the use of the Internet opens up a wide range of challenges such as security (e.g., performing cyber-attacks), risk of data privacy (e.g., data poisoning and inference attacks), etc. The usage of current conventional centralized security measures has limitations in terms of a single point of failure, verifiability, traceability, and scalability. Motivated from the aforementioned challenges, we propose a Secured Privacy-Preserving Framework (SP2F) for smart agricultural UAVs. The proposed SP2F framework has two main engines, a two-level privacy engine, and a deep learning-based anomaly detection engine. In the two-level privacy engine, a blockchain, and smart contract-based enhanced Proof of Work (ePoW) is designed for data authentication, and to mitigate data poisoning attacks. A Sparse AutoEncoder (SAE) is applied for transforming data into a new encoded format for preventing inference attacks. In the anomaly detection engine, a Stacked Long-Short-Term Memory (SLSTM) is used to train and evaluate the results of the proposed two-level privacy engine using two publicly accessible IoT-based datasets, namely ToN-IoT and IoT Botnet. Finally, based on thorough analysis, and comparison, we identify that the SP2F framework outperforms several state-of-the-art techniques in both non-blockchain and blockchain frameworks.
DBTP2SF: A deep blockchain-based trustworthy privacy-preserving secured framework in industrial internet of things systems
Article, Transactions on Emerging Telecommunications Technologies, 2021, DOI Link
View abstract ⏷
By providing ubiquitous connectivity, effective data analytics tools, and better decision support systems for improved market competitiveness, the industrial internet of things (IIoT) promises creative business models in different industrial domains. However, the conventional IIoT architecture can no longer provide adequate support for such an enormous device as the number of nodes, and network size increases. Therefore, several challenges, such as security, privacy, centralization, trust, and integrity prevents faster adaptation of IIoT applications. To address aforementioned challenges, we present a deep blockchain-based trustworthy privacy-preserving secured framework (DBTP2SF) for IIoT environment. This framework comprises of three modules, namely, trust management module, a two-level privacy-preservation module, and an anomaly detection module. In trustworthiness module, blockchain (BC)-based address reputation system is proposed. In the two-level privacy module a BC-based enhanced proof of work technique is simultaneously applied with AutoEncoder, to transform cyber-physical system data into a new reduced form that prevents inference and poisoning attacks. In the anomaly detection module, deep neural network is deployed. Finally, due to various limitations of current Cloud-Fog infrastructure, we present a BC-interplanetary file systems integrated Cloud-Fog architecture, namely, BlockCloud and BlockFog to deploy proposed DBTP2SF framework in IIoT environment. The experiment is conducted using IIoT-based realistic dataset, namely, ToN-IoT. The performance analysis shows that the proposed approach outperforms using transformed dataset over peer privacy-preserving intrusion detection strategies, and has obtained accuracy of 98.97%, and detection rate of 93.87%. Finally, we have shown the superiority of DBTP2SF framework over some of the recent state-of-art techniques in both non-BC and BC-based IIoT system.
Data provenance and access control rules for ownership transfer using blockchain
Article, International Journal of Information Security and Privacy, 2021, DOI Link
View abstract ⏷
Provenance provides information about how data came to be in its present state. Recently, many critical applications are working with data provenance and provenance security. However, the main challenges in provenance-based applications are storage representation, provenance security, and centralized approach. In this paper, the authors propose a secure trading framework which is based on the techniques of blockchain that includes various features like decentralization, immutability, and integrity in order to solve the trust crisis in centralized provenance-based system. To overcome the storage representation of data provenance, they propose JavaScript object notation (JSON) structure. To improve the provenance security, they propose the access control language (ACL) rule. To implement the JSON structure and ACL rules, permissioned blockchain based tool “Hyperledger Composer” has been used. They demonstrate that their framework minimizes the execution time when the number of transaction increases in terms of storage representation of data provenance and security.
A secured distributed detection system based on IPFS and blockchain for industrial image and video data security
Kumar R., Tripathi R., Marchang N., Srivastava G., Gadekallu T.R., Xiong N.N.
Article, Journal of Parallel and Distributed Computing, 2021, DOI Link
View abstract ⏷
Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some amount of modification in images and videos originally uploaded by the copyright holders such as photographers, graphic designers, and video providers. Copyright violators, who are not the original creators of multimedia content modify them using image processing and frame modification techniques such as grayscale conversion, cropping, rotation, frame compression, and frame speed manipulation. Then, upload the tampered images and videos. To address this problem, we propose an IPFS-based (InterPlanetary File System-based) decentralized peer-to-peer image and video sharing platform built on blockchain technology. We use a perceptual hash (pHash) technique to detect copyright violations of multimedia. When multimedia is to be uploaded to the IPFS, the pHash of the same content is determined and checked against existing pHash values in the blockchain network. Similarity with existing pHash values would result in the multimedia being detected as tampered with. Blockchain technology offers the advantage of non-involvement of a third party and consequently the avoidance of a single point of failure.
A Distributed framework for detecting DDoS attacks in smart contract-based Blockchain-IoT Systems by leveraging Fog computing
Kumar P., Kumar R., Gupta G.P., Tripathi R.
Article, Transactions on Emerging Telecommunications Technologies, 2021, DOI Link
View abstract ⏷
With the advancement of blockchain technology, and the proliferation of Internet of things (IoT)-driven devices, the blockchain-IoT applications is changing the perception and working infrastructure of smart networks. Blockchain supports decentralized architecture and provides secure management, authentication, and access to IoT systems by deploying smart contracts provided by Ethereum. The growing demand and expansion of blockchain-IoT systems is generating large volume of sensitive data. Moreover, distributed denial-of-service (DDoS) attacks are the most challenging threats to smart contracts in blockchain-IoT systems. The 2016 decentralized autonomous organization and 2017 parity wallet attacks exposed the critical fault-lines among Ethereum smart contracts. Currently, there is no security mechanism available for smart contracts after its deployment in blockchain-IoT systems. In order to address these challenges, first we use two artificial intelligence techniques, random forest (RF) and XGBoost that gives full autonomy in decision making capabilities in the proposed security framework. Second, for data load balancing and distributed file storage of IoT data, interplanetary file system is suggested. Finally, we are the first to propose a distributed framework based on fog computing to detect DDoS attacks in smart contracts. The performance of the detection system is evaluated using actual IoT dataset, namely, BoT-IoT. The proposed system is evaluated in terms of accuracy (AC), detection rate (DR), and false alarm rate (FAR). The results confirms the superiority of the proposed framework over some of the recent state-of-art techniques in detecting rare attacks. The proposed framework has achieved DR up to 99.99% using RF by using 10 features of BoT-IoT dataset.
PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities
Kumar P., Kumar R., Srivastava G., Gupta G.P., Tripathi R., Gadekallu T.R., Xiong N.N.
Article, IEEE Transactions on Network Science and Engineering, 2021, DOI Link
View abstract ⏷
With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: A two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.
Towards design and implementation of security and privacy framework for Internet of Medical Things (IoMT) by leveraging blockchain and IPFS technology
Article, Journal of Supercomputing, 2021, DOI Link
View abstract ⏷
The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage. Today, this large volume of IoMT generated medical data is stored in the centralized storage system. However, centralization of patient sensitive information leads to a single point of failure, privacy, and security concern. To address these issues, we propose a smart contracts enabled consortium blockchain network. We integrated interplanetary file systems (IPFS) cluster node where smart contracts are deployed at the initial stage for authentication of patient’s and medical devices, the same cluster layer is also proposed as a distributed data storage layer for device-generated data after authentication and these data are securely transmitted over the consortium blockchain. The IPFS cluster node ensures the security and authentication of the devices and it also provides secure storage management in IoMT enabled healthcare system. The consortium network enables the privacy of data owing to hash-based storage in a block of IoMT enabled healthcare network.
Distributed Off-Chain Storage of Patient Diagnostic Reports in Healthcare System Using IPFS and Blockchain
Kumar R., Marchang N., Tripathi R.
Conference paper, 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 2020, DOI Link
View abstract ⏷
The healthcare industry electronically maintains medical data which includes patients' information such as patients' personal information, diagnostic reports, and doctor prescriptions. However, the centralized storage model is currently used for storing such sensitive information. One main disadvantage of the centralized model is the difficulty in preserving user privacy. Threats relating to user (patient) privacy include unauthorized access of critical information such as identity details and diseases from which a patient is suffering, and misuse of patients' data and their medical reports. To address this issue, we propose a distributed off-chain storage of medical data using IPFS (Interplanetary File System) and blockchain technology. The proposed framework while preserving patient privacy facilitates easy access of medical data by authorized entities such as healthcare providers (e.g., doctors and nurses). Moreover, it achieves consistency, integrity, and availability.
Secure healthcare framework using blockchain and public key cryptography
Book chapter, Advances in Information Security, 2020, DOI Link
View abstract ⏷
In the current scenario, healthcare organizations are facing a serious problem in sharing medical information among different stakeholders without sacrificing the privacy and integrity of information. To store and share such a large volume of healthcare information securely is an important research issue. Blockchain has been used successfully in the bitcoin for the decentralized exchange of cryptocurrency without the involvement of the third party. The new structure of blockchain has been designed to accommodate the need of another field such as healthcare. Blockchain technology has the potential of immutability, integrity and decentralized architecture to manage the health records of the patient. This paper aims to address the issues of data security and authentication in healthcare. We have proposed a blockchain based secure framework using public key cryptography.
Blockchain-Based Framework for Data Storage in Peer-to-Peer Scheme Using Interplanetary File System
Book chapter, Handbook of Research on Blockchain Technology, 2020, DOI Link
View abstract ⏷
Interplanetary file system (IPFS) is a version-controlled file system in a peer-to-peer scheme that provides a content-addressable block storage model for storing and sharing files in a distributed environment. According to the feature of IPFS, we have proposed a blockchain-based framework to share the file using content-addressable block storage in the peer-to-peer model. To overcome the availability, reliability, storage overhead, and another issue of centralized service providers, we have introduced blockchain- and IPFS-based distributed storage model that ensures the immutability, integrity, and availability of the resource. In this framework, we are storing the file on IPFS and the addressable content (hash) to the blockchain as a transaction. Our proposed scheme can effectively solve the problem of availability, reliability, and storage of a centralized service provider. In this proposed framework, we are storing the academic record of the student (jpg format) on IPFS (peer-to-peer scheme). To implement the blockchain framework, we have used python(anaconda), python flask, postman, and IPFS.
Content-Based Transaction Access From Distributed Ledger of Blockchain Using Average Hash Technique
Book chapter, Opportunities and Challenges for Blockchain Technology in Autonomous Vehicles, 2020, DOI Link
View abstract ⏷
There are many critical applications working with blockchain-based technology including the financial sector, healthcare, and supply chain management. The fundamental application of blockchain is Bitcoin, which was primarily designed for the financial value transfer. Owing to the feature of decentralized storage structure, immutability, integrity, availability, and reliability of transactions, the blockchain has become the need of the current industry like VANET. However, presently, not much work has been done in order to mitigate the redundancy in the distributed ledger. Hence, the authors arrive at the intelligible conclusion to detect a similar transaction that can mitigate the redundancy of transaction in a distributed ledger. In this chapter, they are addressing two main challenges in blockchain technology: firstly, how to minimize the storage size of blockchain distributed ledger and, secondly, detecting the similar transaction in the distributed ledger to mitigate the redundancy. To detect similar transaction from the distributed ledger they have applied the average hash technique.
A secure and distributed framework for sharing COVID-19 patient reports using consortium blockchain and IPFS
Conference paper, PDGC 2020 - 2020 6th International Conference on Parallel, Distributed and Grid Computing, 2020, DOI Link
View abstract ⏷
Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.
Large-scale data storage scheme in blockchain ledger using IPFS and NoSQL
Book chapter, Large-Scale Data Streaming, Processing, and Blockchain Security, 2020, DOI Link
View abstract ⏷
The future applications of blockchain are expected to serve millions of users. To provide variety of services to the users, using underlying technology has to consider large-scale storage and assessment behind the scene. Most of the current applications of blockchain are working either on simulators or via small blockchain network. However, the storage issue in the real world is unpredictable. To address the issue of large-scale data storage, the authors have introduced the data storage scheme in blockchain (DSSB). The storage model executes behind the blockchain ledger to store large-scale data. In DSSB, they have used hybrid storage model using IPFS and MongoDB(NoSQL) in order to provide efficient storage for large-scale data in blockchain. In this storage model, they have maintained the content-addressed hash of the transactions on blockchain network to ensure provenance. In DSSB, they are storing the original data (large-scale data) into MongoDB and IPFS. The DSSB model not only provides efficient storage of large-scale data but also provides storage size reduction of blockchain ledger.
Big-data driven approaches in materials science: A survey
Tripathi M.K., Kumar R., Tripathi R.
Conference paper, Materials Today: Proceedings, 2019, DOI Link
View abstract ⏷
The data volume is growing rapidly in material science. Every year data volume is getting double in many context of material science. The growing rate of data in material science is demanding for new computational infrastructures that can speed-up material discovery and deployment. In this survey, we are focusing on the challenges in material science due to growing data rate, and how Big Data technology can play a major role in research of material science. This survey includes various disciplines that can be used with Big Data to provide better analysis in the material science research.
Traceability of counterfeit medicine supply chain through Blockchain
Conference paper, 2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019, 2019, DOI Link
View abstract ⏷
The main issues with drug safety in the counterfeit medicine supply chain, are to do with how the drugs are initially manufactured. The traceability of right and active pharmaceutical ingredients during actual manufacture is a difficult process, so detecting drugs that do not contain the intended active ingredients can ultimately lead to end-consumer patient harm or even death. Blockchain's advanced features make it capable of providing a basis for complete traceability of drugs, from manufacturer to end consumer, and the ability to identify counterfeit-drug. This paper aims to address the issue of drug safety using Blockchain and encrypted QR(quick response) code security.
Implementation of Distributed File Storage and Access Framework using IPFS and Blockchain
Conference paper, Proceedings of the IEEE International Conference Image Information Processing, 2019, DOI Link
View abstract ⏷
Many critical applications are designed on the distributed structure using the blockchain technology to ensure the availability, immutability, and security. However, these applications are facing the storage problem owing to the data volume growth of transaction. The number of transactions and its size in a block is growing in the blockchain day by day because of the feature of immutability and append-only. The growing nature of transactions in a block is not only making the problem for storage but also in access to the block transactions. In this paper, we propose an IPFS based blockchain storage model to solve the storage problem of transaction in a block along with access of transaction of a particular block. In the propose storage model, the miners stores transaction on IPFS distributed file system storage and get the returned IPFS hash of transaction into the block of the blockchain. The feature of the IPFS network and its resultant hash reduce the size of transactions in a block. To secure access of transaction for a particular block content-addressed (IPFS hash) storage technique has been proposed. We have applied this scheme on a transaction which includes image storage on IPFS and hash storage into the blockchain. In this paper, we have also proposed the content-addressed technique in contrast to the location addressed for the access of transaction. To implement the framework we have used anaconda python, python flask, and IPFS.
