Intelligent transportation systems (ITS) improve vehicle connectivity, traffic efficiency, and road safety. Conversely, quick and safe vehicle authentication still poses a significant issue, especially at the handover time when switching between roadside units (RSUs), where network efficacy is influenced by computational overhead and re-authentication delays. To overcome these issues, this paper proposes DYNAMIC-TRUST. This blockchain-based authentication framework relies on the Proof of Trust (PoT) consensus mechanism to avoid redundant re-authentication, minimizing computation and communication costs. Compared to conventional authentication approaches, our method decentralizes vehicle revocation, allowing RSUs to revoke compromised vehicles autonomously without relying on a trusted authority, providing resilience regardless of adversarial conditions. The proposed framework’s resistance to identity theft, replay, and Sybil attacks has been proven by formal security analysis using Scyther and the Real-Or-Random (ROR) oracle model. Also, the Simulation of Urban Mobility (SUMO) is used to evaluate real-world practicality, proving improved scalability, lowered authentication latency, and greater network efficiency over various vehicular circumstances. Blockchain’s potential for enhancing vehicular network performance, trust, and security is highlighted in this study, which helps to develop smart cities and 6G-enabled Internet of Vehicles (IoV) infrastructures.

Intelligent Transport Systems (ITS) represent a burgeoning and transformative concept aimed at reshaping the landscape of mobility both within and outside cities. The Internet of Vehicles (IoV) serves as a networked ecosystem that integrates infrastructure, pedestrians, fog, cloud, and vehicles to enhance the capabilities of ITS. While IoV holds tremendous promise for advancing transportation systems, its networked and data-centric nature raises numerous security concerns. Several solutions have recently been proposed to address these IoV-related challenges; however, many of them involve significant computational overhead and exhibit security flaws. Moreover, there is concern about malicious vehicles infiltrating the network and potentially gaining unauthorized access to services. To tackle these challenges, we present SEATS, a ground breaking solution. The system aims to ensure the secure exchange of information, authentication by both parties, and effective key management among vehicles, roadside units (RSU), and cloud servers. We conduct extensive security and privacy assessments on the proposed approach using the Real-or-Random (ROR) oracle model and Scyther tools, supplemented by an informal security study. The framework is simulated using the Objective Modular Network Testbed in C++ (OMNet++). To demonstrate the efficacy of our approach, we compare it to existing methods, evaluating computation and communication costs.

AIoT in medicine, or the combination of artificial intelligence (AI) and the internet of things (IoT), is transforming healthcare delivery and patient outcomes. This chapter contains a collection of real- world case studies and success stories demonstrating the revolutionary power of AIoT technology in various medical fields. In these instances, the focus is on how AIoT boosts diagnosis accuracy, enables personalised treatment regimens, and improves operational efficiencies in healthcare organisations. Key case studies include using AIoT in remote patient monitoring, where continuous data gathering from wearable devices, paired with AI algorithms, enables real- time health tracking and early intervention. Another example involves the application of AIoT in the predictive maintenance of medical equipment, which reduces downtime and ensures the availability of key items. Furthermore, the authors investigate the significance of AIoT in optimising hospital workflows, such as expediting patient admissions and inventory management using smart sensors and automated systems.

Autonomous vehicles (AVs) play a significant role in intelligent transportation systems (ITS), which handle vehicles without human interference. The AVs are integrated with the Internet of Vehicles (IoV) to connect with more vehicles, sensors, and fog servers and share data. This makes the vehicles vulnerable to attacks and leads to unauthorized access. To overcome this drawback, we introduced biometric authentication for secure communication of vehicles, roadside units (RSUs), and fog servers. In this protocol, we generated two session keys between entities. The communication cost, computation cost, and security parameters are compared with existing methods to show that the proposed protocol is more efficient than others. Finally, the formal and informal security analysis ensures the proposed protocol is more secure.

This paper highlights a novel approach to address multiple networking-based VM allocation and migration objectives at the cloud data center. The proposed approach in this paper is structured into three distinct phases: firstly, we employ a Bi-Directional Long Short Term Memory (BiLSTM) model to predict Virtual Machines (VMs) instance's prices. Subsequently, we formulate the problem of allocating VMs to Physical Machines (PMs) and switches in a network-aware cloud data center environment as a multi-objective optimization task, employing Linear Programming (LP) techniques. For optimal allocation of VMs, we leverage the Branch-and-Bound (BaB) technique. In the third phase, we implement a VM migration strategy sensitive to SLA requirements and energy consumption considerations. The results, conducted using the CloudSim simulator, demonstrate the efficacy of our approach, showcasing a substantial 35% reduction in energy consumption, a remarkable decrease in SLA violations, and a notable 18% increase in the cloud data center's profit. Finally, the proposed multi-objective approach reduces energy consumption and SLA violation and makes the data center sustainable.

With the rapid growth of Android applications, ensuring robust security has become a critical concern. Traditional Vulnerability Assessment and Penetration Testing (VAPT) approaches, though effective across platforms, often fall short in addressing Android-specific security challenges. This paper presents a comprehensive review of security testing methods tailored to the Android ecosystem, including static and dynamic analysis, hybrid approaches, network communication testing, reverse engineering, malware detection, and permission-based assessments. Android's open-source nature, device fragmentation, and inconsistent security policies introduce unique vulnerabilities that require specialized testing strategies. By examining current tools, methodologies, and best practices, this review identifies recurring gaps in the Android application security testing process. It highlights the need for more adaptable and thorough testing frameworks. The insights provided are valuable to developers, researchers, and security professionals aiming to strengthen Android app security. Ultimately, this work underscores the importance of tailoring security assessment practices to the evolving threat landscape of the Android platform, thereby contributing to the development of safer and more resilient applications.

Digital Twin (DT) technology is a digital illustration of a physical object or system; this technology has paid much attention to IoT, healthcare, automotive manufacturing, construction of buildings, and even cities. However, these applications may also have serious security pitfalls in DT deployment. Distributed Denial of Service (DDoS) attacks significantly threaten the availability and stability of computer networks and services. Detecting and mitigating these attacks on time is crucial for maintaining network security. This chapter aims to develop an algorithmic-based approach for detecting and preventing DDoS attacks in the initial stages of Network Function Virtualisation (NFV). The proposed model involves the Network traffic collected from a sender in various monitoring points within the network infrastructure. Then the traffic is analysed by extracting relevant information like from which source the traffic is coming, Transmission Control Protocol (TCP), three-way handshake details, packet size, and traffic volume. The developed model is deployed in real time to monitor incoming network traffic. It analyses the extracted features and compares them with the learned patterns to identify potential Distributed Denial of Service attacks (DDoS). Alerts and notifications are generated, and warning notifications will be given to the source node. Upon detection of a DDoS attack, appropriate mitigation strategies are implemented to protect the network infrastructure and services. These may include traffic filtering; and rate limiting to mitigate the attack’s impact and ensure critical resource availability. The performance metrics, such as detection accuracy, false positive rate, and response time, will be measured to assess the reliability and efficiency of the solution, by developing an algorithmic model that can effectively detect and mitigate Distributed Denial of Service attacks. This chapter aims to enhance network security and ensure the uninterrupted availability of online services for digital twin environments, even in the face of evolving and sophisticated cyber threats.

Cloud computing is a relatively recent technological development that has steadily gained popularity over the last few years. The data sharing between legitimate entities in peer-to-peer (P2P) file systems in the cloud is the most challenging task. Nowadays, many individuals utilize document destruction programs. Sharing files created with these programs is one way for individuals to make money online. Some websites, like Chegg and Scribd, provide a forum for academics and freelancers to share their work with the public. To participate in these programs, the users first become members. And those who wish to access the files must still pay for the application rather than the original author. In this chapter, we developed a novel approach for a secure file-sharing system called blockchain-enabled and cloudlet-based networks (BCECBN). It integrates a blockchain for secure transactions and access to shared files. The proposed model is protected against various attacks performed by the adversary. In addition, it provides a solution for the above-discussed examples; users may easily share and trade data with one another and save and share data over the internet with no effort. In addition, this research will examine a cloud-based secure file-sharing system and exchange the data using digital media. Each user performs a transaction along the blockchain to get access to the files.

In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.

This paper comprehensively analyzes Facebook data, a rich source of valuable information within big data. The study encompasses data collection, preprocessing, and exploratory analysis of a substantial dataset derived from Facebook interactions and activities. Through advanced data processing techniques and statistical methodologies, we unveil meaningful insights into user behavior, content engagement, and patterns on the platform. This analysis has significant implications for understanding user preferences, trends, and the dynamics of social networking in the digital age. The study revealed valuable trends, patterns, and metrics related to user interactions, posting habits, etc. Integrating Hive commands for data analysis and R programming for visualization offered a powerful synergy that made the findings accessible and visually compelling. The project underscores the importance of big data analytics in unraveling the hidden dimensions of social media and offers a practical demonstration of the power of data-driven decision-making. The findings and visualizations derived from this analysis shed light on the vast landscape of Facebook, enabling informed decisions and future research in social media analytics.

The advancements in telecommunications are significantly benefiting the Internet of Vehicles (IoV) in various ways. Minimal latency, faster data transfer, and reduced costs are transforming the landscape of IoV. While these advantages accompany the latest improvements, they also expand cyberspace, leading to security and privacy concerns. Vehicles rely on trusted authorities for registration and authentication processes, resulting in bottleneck issues and communication delays. Moreover, the central trusted authority and intermediate nodes raise doubts regarding transparency, traceability, and anonymity. This paper proposes a novel vehicle authentication handover framework leveraging blockchain, IPFS, and hybrid computing. The framework uses a Proof of Reputation (PoR) consensus mechanism to improve transparency and traceability and the Elliptic Curve Cryptography (ECC) cryptosystem to reduce computational delays. The suggested system assures data availability, secrecy, and integrity while maintaining minimal latency throughout the vehicle re-authentication. Performance evaluations show the system's scalability, with creating keys, encoding, decoding, and registration operations done rapidly. Simulation is performed using SUMO to handle vehicle mobility in an IoV environment. The findings demonstrate the practicality of the proposed framework in vehicular networks, providing a reliable and trustworthy approach for IoV communication.

This paper proposes a novel approach to enhancing education systems by integrating federated learning techniques with big data analytics. Traditional data analysis methods in educational settings often need help regarding data privacy, security, and scalability. Federated learning addresses these issues by enabling collaborative model training across distributed datasets without data centralization, thus preserving the privacy of sensitive information. By harnessing the vast amounts of educational data generated from various sources such as online learning platforms, student information systems, and academic applications, federated learning empowers educational institutions to derive valuable insights while respecting data privacy regulations. Leveraging the collective intelligence of decentralized data sources, federated learning algorithms facilitate the development of robust predictive models for student performance, personalized learning recommendations, and early intervention strategies. Moreover, federated learning enables continuous model improvement by aggregating local model updates from participating institutions, ensuring adaptability to evolving educational landscapes. This paper explores the technical foundations of federated learning, its application in education systems, and its potential benefits in improving learning outcomes and fostering data-driven decision-making in education. Through a comprehensive review of existing literature and case studies, this research aims to provide insights into the opportunities and challenges associated with implementing federated learning-based big data analytics in education systems, ultimately paving the way for a more efficient and personalized approach to education.

Modern living is significantly impacted by intelligent transportation systems (ITS), which have the potential to alter how transportation is maintained and improve multiple facets of day-to-day mobility while also increasing security, effectiveness, and convenience. ITS offers the fundamental framework and technology necessary for IoV to operate efficiently. The IoV ecosystem foundation is the integration of sensors, communication networks, architectural components, and data analyses from ITS, which enables vehicles to join a connected, intelligent transportation network. Although ITS and IoV have many advantages, the increasing connectivity and data sharing also pose security risks, including those related to eavesdropping, authentication, privacy, and data integrity. To address these issues, we developed the novel, lightweight SAKM-ITS protocol, which enables authentication and key management between vehicles, roadside units (RSUs), and cloud servers. Using Scyther and Tamarin Prover tools, the protocol security is tested. For different attacks, an informal security study is also conducted. We also compared the findings with other recent computing and communication costs studies.

The utilization of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially in recent years over traditional authentication systems. Since the biometric traits are irrevocable, two important issues such as security and privacy still need to be addressed in BAS. Researchers explore homomorphic encryption (HE) to propose several privacy-preserving BAS. However, the correctness of the evaluated results computed by the cloud server on the protected templates is still an open research challenge. These methods are able to conserve the privacy of biometric templates but unable to check the correctness of computed result results in false reject or accept. To overcome this issue, we suggest a reliable and privacy-preserving verifiable multi-instance iris verification system using Paillier HE and one-digit checksum (PVMIAPO). Modified local random projection is implemented on the fused iris template to produce the reduced template. Later, Paillier HE is applied on the reduced template to create the protected template. The result returned by the third party server is verified using the one-digit checksum. The efficiency of PVMIAPO is verified by experimenting with it on SDUMLA-HMT, IITD, and CASIA-V3-Interval iris databases. PVMIAPO gratifies the irreversibility, diversity, and revocability properties. PVMIAPO also obtains fair performance in contrast to the existing methods.

The smart healthcare system advancements have introduced the Internet of Things, enabling technologies to improve the quality of medical services. The main idea of these healthcare systems is to provide data security, interaction between entities, efficient data transfer, and sustainability. However, privacy concerning patient information is a fundamental problem in smart healthcare systems. Many authentications and critical management protocols exist in the literature for healthcare systems, but ensuring security still needs to be improved. Even if security is achieved, it still requires fast communication and computations. In this paper, we have introduced a new secure privacy-enhanced fast authentication key management scheme that effectively applies to lightweight resource-constrained devices in healthcare systems to overcome the issue. The proposed framework is applicable for quick authentication, efficient key management between the entities, and minimising computation and communication overheads. We verified our proposed framework with formal and informal verification using BAN logic, Scyther simulation, and the Drozer tool. The simulation and tool verification shows that the proposed system is free from well-known attacks, reducing communication and computation costs compared to the existing healthcare systems.

In upcoming iterations of the internet of vehicles (IoVs), seamless communication will be facilitated among individuals, vehicles, roadside units (RSUs), and communication platforms. The overarching objectives include enhancing transit efficiency, ensuring comfort, improving road safety, and concurrently fostering environmental conservation. This research introduces a secure fog service for vehicular ad hoc networks (VANETs), enabling diverse traffic data services such as road alerts, congestion control, and autonomous driving. The authors propose a novel authentication approach for fog services. Leveraging physical unclonable function (PUF) and blockchain, this approach facilitates authentication between vehicles and road-side units (RSU), circumventing potential fraudulent fog nodes. A comprehensive security analysis demonstrates its resilience against known attacks. Comparative evaluation against existing approaches underscores our protocol's superior balance of security and overhead, making it well-suited for secure vehicle fog environments.

For the safety and reliability of the system, Remaining Useful Life (RUL) prediction is considered in many industries. The traditional machine learning techniques must provide more feature representation and adaptive feature extraction. Deep learning techniques like Long Short-Term Memory (LSTM) achieved an excellent performance for RUL prediction. However, the LSTM network mainly relies on the past few data, which may only capture some contextual information. This paper proposes a hybrid combination of Convolution Neural Network (CNN) and LSTM (CNN+LSTM) to solve this problem. The proposed hybrid model predicts how long a machine can operate without breaking down. In the proposed work, 1D horizontal and vertical signals of the mechanical bearing are first converted to 2D images using Continuous Wavelet Transform (CWT). These 2D images are applied to CNN for key feature extraction. Ultimately, these key features are applied to the LSTM deep neural network for predicting the RUL of a mechanical bearing. A PRONOSTIA data is utilized to demonstrate the performance of the proposed model and compare the proposed model with other state-of-the-art methods. Experimental results show that our proposed CNN+LSTM-based hybrid model achieved higher accuracy (98%) with better robustness than existing methods.

Reversible data hiding (RDH) based on prediction error expansion (PEE) needs a reliable predictor to forecast the pixel. The hidden information is inserted into the original cover image pixels using the Prediction Error (PE). To improve the accuracy of pixel predictions for cover images, there are a number of algorithms available in the literature. Based on the different gradient estimations, several academics have suggested prediction methods. More research on this gradient-based pixel prediction method is presented in this article. In order to improve exploration gradient estimates, we have looked at a number of local contexts surrounding the current pixel. It has been stated that experiments have been conducted to evaluate the effect of different neighborhood sizes on gradient estimation. Additionally, we investigate two methods for choosing paths according to gradient magnitudes. To incorporate the data into the initial pixels, a new embedding technique called Prediction Error Expansion has been suggested. In the context of reversible data concealment, experimental results point towards a better gradient based prediction employing an prediction embedding technique.

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

As the cyber theft is increases, information safety and confidentiality are the major issues in wireless communications. 6G technology overcomes these difficulties to build a secured intelligent transportation system (ITS). Conventional transportation system faces high computation cost when road side unit (RSU) process each authentication vehicle request. In this chapter, to address this issue we introduced batch authentication and key exchange to secure user privacy and prevent attacks. To ensure message integrity, this system provides location-based information safely from RSU to vehicle without any changes. This system reduces the communication and computational costs. Simulation is performed using simulation of urban mobility (SUMO) simulator.

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.

Mobile payments makeup one of the fastest-growing mobile services available today and are widely used by smartphones for utility payments, bill payments, and online shopping, among other applications. Mobile payments are playing a vital role in the fast growth of online markets and are revolutionizing the supply chain of businesses and industries. Mobile payments are becoming dominant compared to conventional off-line mode payment channels and online e-channels such as ATM, e-cheque, and e-card payments. The success of e-business depends on several factors, including the type of mobile payment channel used, the associated security infrastructure, the stakeholders involved, and the m-business models adopted. In this paper, we present a systematic literature review (SLR) of mobile payments and characterize the state-of-the-art research conducted in this area, covering articles published during the past two decades, from 2000 to 2020. Following the SLR process, we examined over 350 research papers with a comprehensive and detailed inspection of the mobile payment domain’s literature. Based on the analysis, we present the trends, patterns, new technologies, innovations, gaps in the existing literature, and critical challenges. The recommendations given will help identify the primary areas requiring advancement in future research on mobile payment systems.

A vishing attack is a category of Phishing attack in which the attacker attempts to capture clandestine information through a phone call or Short Message Service (SMS). These types of attacks mostly target financial information and uneducated people are victims. In this paper, a user friendly security mechanism is proposed for preventing vishing attack in banking system under one nation. The proposed authentication mechanism uses a Central Banking Server (CBS) which act as an Authentication Server (AS) and a nationwide unique phone number. The proposed approach is simulated and analyzed by means of Scyther which is a protocol verification tool and the results show that our mechanism is more protected and harmless from vishing attacks.

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior.

Daily digital payments in Financial Technology (FinTech) are growing exponentially. A huge demand is for developing secure, lightweight cryptography protocols for wearable IoT-based devices. The devices hold the consumer information and transit functions in a secure environment to provide authentication and confidentiality using contactless Near-Field Communication (NFC) or Bluetooth technologies. On the other hand, Security breaches have been observed in various dimensions, especially in wearable payment technologies. In this paper, we developed a threat model in the proposed framework and how to mitigate these attacks. This study accepts the three-authentication factor, as biometrics is one of the user's most vital authentication mechanisms. The scheme uses an “Elliptic Curve Integrated Encryption Scheme (ECIES)”, “Elliptic Curve Digital Signature Algorithm (ECDSA)” and “Advanced Encryption Standard (AES)” to encrypt the messages between the entities to ensure higher security. The security analysis of the proposed scheme is demonstrated through the Real-or-Random oracle model (RoR) and Scyther's widely accepted model-checking tools. Finally, we present a comparative summary based on security features, communication cost, and computation overhead of existing methods, specifying that the proposed framework is secure and efficient for all kinds of remote and proximity payments, such as mini, macro, and micro-payments, using wearable devices.

Wearable devices are parts of the essential cost of goods sold (COGS) in the wheel of the Internet of things (IoT), contributing to a potential impact in the finance and banking sectors. There is a need for lightweight cryptography mechanisms for IoT devices because these are resource constraints. This paper introduces a novel approach to an IoT-based micro-payment protocol in a wearable devices environment. This payment model uses an “elliptic curve integrated encryption scheme (ECIES)” to encrypt and decrypt the communicating messages between various entities. The proposed protocol allows the customer to buy the goods using a wearable device and send the mobile application’s confidential payment information. The application creates a secure session between the customer, banks and merchant. The static security analysis and informal security methods indicate that the proposed protocol is withstanding the various security vulnerabilities involved in mobile payments. For logical verification of the correctness of security properties using the formal way of “Burrows-Abadi-Needham (BAN)” logic confirms the proposed protocol’s accuracy. The practical simulation and validation using the Scyther and Tamarin tool ensure that the absence of security attacks of our proposed framework. Finally, the performance analysis based on cryptography features and computational overhead of related approaches specify that the proposed micro-payment protocol for wearable devices is secure and efficient.

Deep learning shown its potential in a variety of medical applications and proved as a count on by people as a step ahead approach compared to traditional machine learning models. Moreover, the other implementations of these models such as the convolutional neural networks (CNNs) provide extensive applications in the field of medicine, which usually involves processing and analysis of a large dataset. This paper aims to create a CNN model which can solve the problem of white blood cell subtyping which is a daunting one in clinical processing of blood. The manual classification of white blood cells in laboratory is a time-consuming process which gives rise to the need for an automated process to perform the task. A CNN-based machine learning model is developed to classify the leukocytes into their proper subtypes by performing tests on a dataset of around twelve thousand images of leukocytes and their types, and a wide range of parameters is evaluated. This model can automatically classify the white blood cells to save manual labor, time and improve efficiency. Further, pretrained models like Inception-v3, VGGNet and AlexNet are used for the classification, and their performance is compared and analyzed.

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its ef ficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, rel ative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.

The flow of information is a valuable asset for every company and its consumers, and Distributed Denial-of-Service (DDoS) assaults pose a substantial danger to this flow. If we do not secure security, hackers may steal information flowing across a network, posing a danger to a business and society. As a result, the most effective ways are necessary to deal with the dangers. A DDoS attack is a well-known network infrastructure assault that prevents servers from servicing genuine customers. It is necessary to identify and block a DDoS assault before it reaches the server in order to avoid being refused services. This prompted us to develop a unique way for detecting and preventing DDoS attacks at the router level in a Software-Defined Network (SDN) environment. This study demonstrates how the method efficiently integrates the first and second signatures in SDN infrastructure domains to identify and prevent DDoS attacks. It also proposes an Early DDoS Detection and Prevention (EDDDeP)-based approach for detecting and blocking malicious traffic in an SDN context. This article covers the EDDDeP, which assists in identifying and preventing DDoS in SDN to prevent malicious traffic from reaching its intended target. As a consequence, the DDoS assault is ultimately contained inside the environment, eliminating superfluous traffic in the DDoS network architecture. This method offers a unique technique to detect a DDoS assault and notify nearby neighbours in order to avert server damage.

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain based security framework is proposed for sharing digital images in a multi user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.

Intelligent transport systems amalgamated with advanced technologies are an important element of the automotive industry, including critical infrastructure and transportation. Internet of Vehicles (IoV) is the modern technological framework designed for intelligent transportation. IoV creates a network of information relations among vehicles, thus contributing to reduced congestion, roadside infrastructure, driver/traveller safety, and traffic efficiency through wireless communication and sensing technology. However, a significant challenge in IoV applications is security, as criminals could potentially exploit these applications. It is clear that despite increasing industry awareness, the potential danger posed by security vulnerabilities and cyber threats is high. In this study, we have designed a new system called AKAP-IoV, which supports secure communication, mutual authentication, and key management among vehicles, roadside units, and fog and cloud servers. AKAP-IoV was tested and verified using Scyther and Tamarin to ensure its resistance to cyber threats. Furthermore, we conducted a formal security analysis using the Real-or-Random (RoR) oracle model to assess security properties logically. In addition, a detailed, comprehensive comparative study was considered to evaluate the performance, functionality, efficiency and security features supported by AKAP-IoV compared to those of recently developed schemes.

Iris recognition technologies applied to produce comprehensive and correct biometric identification of people in numerous large-scale data of humans. Additionally, the iris is stable over time, i.e., iris biometric knowledge offers links between biometric characteristics and people. The e-business and e-governance require more machine-driven iris recognition. It has millions of iris images that are in near-infrared illumination. It is used for people’s identity. A variety of applications for surveillance and e-business will embody iris pictures that are unit non-heritable below visible illumination. The self-learned iris features are created by the convolution neural network (CNN), give more accuracy than handcrafted feature iris recognition. In this paper, a modified iris recognition system is introduced using deep learning techniques along with multi-class SVM for matching. We use the Poly-U database, which is from 209 subjects. CNN with softmax cross-entropy loss gives the most accurate matching of testing images. This method gives better results in terms of EER. We analyzed the proposed architecture on other publicly available databases through various experiments.

Lattice-based crypto systems are regarded as secure and believed to be secure even against quantum computers. lattice-based cryptography relies upon problems like the Shortest Vector Problem. Shortest Vector Problem is an instance of lattice problems that are used as a basis for secure cryptographic schemes. For more than 30 years now, the Shortest Vector Problem has been at the heart of a thriving research field and finding a new efficient algorithm turned out to be out of reach. This problem has a great many applications such as optimization, communication theory, cryptography, etc. This paper introduces the Shortest Vector Problem and other related problems such as the Closest Vector Problem. We present the average case and worst case hardness results for the Shortest Vector Problem. Further this work explore efficient algorithms solving the Shortest Vector Problem and present their efficiency. More precisely, this paper presents four algorithms: the Lenstra-Lenstra-Lovasz (LLL) algorithm, the Block Korkine-Zolotarev (BKZ) algorithm, a Metropolis algorithm, and a convex relaxation of SVP. The experimental results on various lattices show that the Metropolis algorithm works better than other algorithms with varying sizes of lattices.

With the rapid growth of the Internet and smartphone and wireless communication-based applications, new threats, vulnerabilities, and attacks also increased. The attackers always use communication channels to violate security features. The fast-growing of security attacks and malicious activities create a lot of damage to society. The network administrators and intrusion detection systems (IDS) were also unable to identify the possibility of network attacks. However, many security mechanisms and tools are evolved to detect the vulnerabilities and risks involved in wireless communication. Apart from that machine learning classifiers (MLCs) also practical approaches to detect intrusion attacks. These MLCs differentiated the network traffic data as two parts one is abnormal and other regular. Many existing systems work on the in-depth analysis of specific attacks in network intrusion detection systems. This paper presents a comprehensive and detailed inspection of some existing MLCs for identifying the intrusions in the wireless network traffic. Notably, we analyze the MLCs in terms of various dimensions like feature selection and ensemble techniques to identify intrusion detection. Finally, we evaluated MLCs using the “NSL-KDD” dataset and summarize their effectiveness using a detailed experimental evolution.

Abstract: Ownership evidence can be directly exposed through visible watermarks which can avoid the attempts of copyright abuses. JPEG images are widely used for many commercial applications in Internet, because of its reduced size and the requirement of low bandwidth. In this paper, a visible watermarking scheme is proposed for JPEG compressed images. This scheme is based on the modification of frequency coefficients of original image with respect to the frequency coefficients of watermark image. In JPEG compression, forward discrete cosine transform (DCT) phase is processed to embed visible watermarks. Different sizes of color and grayscale watermark images can be embedded in the carrier image with high visibility. Experimental results confirm the quality of the proposed visible watermarking scheme.

The authentication system plays a crucial role in the context of GLObal MObility NETwork (GLOMONET) where Mobile User (MU) often need to seamless and secure roaming service over multiple Foreign Agents (FA). However, designing a robust and anonymous authentication protocol along with a user privacy is essential and challenging task. Due to the resource constrained property of mobile terminals, the broadcast nature of a wireless channel, mobility environments are frequently exposed to several attacks. Many researchers focus their interests on designing an efficient and secure mobile user authentication protocol for mobility networks. Very recently (in 2018), Xu et al presented the novel anonymous authentication system for roaming in GLOMONET, and insisted that their protocol is more secure than existing authentication protocols. The security strength of Xu et al.’s authentication protocol is analysed and identified that the protocol is vulnerable to stolen verifier attack, privileged insider attack, impersonation attack and denial of service attack. In-fact, the protocol suffers from clock synchronization problem and cannot afford local password-verification to detect wrong passwords quickly. As a remedy, we proposed an efficient and robust anonymous authentication protocol for mobility networks. The proposed mobile user authentication protocol achieves the provable security and has the ability to resist against numerous network attacks. Besides, the correctness of the novel authentication protocol is validated using formal security tool called AVISPA (Automated Validation of Internet Security Protocols & Applications). Finally, the performance analysis and simulation results reveals that the proposed authentication protocol is computationally efficient and practically implementable in resource limited mobility environments.

In the mobile payment systems flow of confidential data is one of the essential and vital services. The customer's sensitive data is always kept safe from the various kind of attacks, such as phishing and man-in-the-middle attacks. The current mobile authentication protocols put an extra burden on mobile device users to detect and avoid phishing attacks. In this paper, we propose a novel authentication protocol that deals with an Authentication Server (AS), which sends a nonce message to the mobile customer device to be signed, so that he/she can avoid phishing attacks. The phishing attacks are fraudulent e-mail messages appearing to come from legitimate enterprises to access the private information and to commit identity theft. On the other hand, over the Internet, so many associated attacks are also possible, and it can quickly spread across the Internet and cause severe damage to our society. In this paper, we mainly focus on a phishing attack in the mobile environment with the help of an authentication server. Our We simulate our proposed approach with the verification model checking tool Scyther, which rigorously analyses our proposed scheme and shows that our proposed method is secure and safe from phishing attacks.

Near Field Communication (NFC) is one of the fast-growing technologies related to proximity-based mobile payments. In this paper, a secure NFC-enabled payment model that can be used for peer-to-peer (P2P) payments and payer-to-merchant (P2M) payments is presented. This payment model uses elliptic curve cryptography (ECC) to encrypt customer data. The proposed protocol provides end-to-end secure communication between customer and merchant through the bank using a reader and writer application. In our proposed model, the primary objective is that the users enter the customer PIN and the amount in their own NFC devices and it is the responsibility of the acquiring bank to rechecked and validated the amount of the transaction on the merchant's device. The proposed model is convenient to use as the customers simply need to enter information on their NFC phones and tap it onto the merchant's NFC device. Further, the proposed approach is verified for its security features and validated for its correctness using formal methods of the theoretically proving by Burrows–Abadi–Needham (BAN) logic, and simulation by using automated validation of Internet security protocols (AVISPA), Scyther and Tamarin. Moreover, the proposed protocol provides more security attributes and incurs fewer communication costs and low computational overhead compared to existing NFC payment protocols used for real-world applications.

Short message service (SMS) provides a wide channel of communication for banking in mobile commerce and mobile payment. The transmission of SMS is not secure in the network using global system for mobile communications or general packet radio service. Security threats in SMS restricted the use of SMS in mobile banking within certain limits. This paper proposed a model to address the security of SMS using elliptic curve cryptography. The proposed model provides end-to-end SMS communication between the customer and the bank through the mobile application. The main objective of the proposed model is to design and develop a security framework for SMS banking. Further, the protocol is verified for its correctness and security properties because most of the protocols are not having the facility to be verified by using the formal methods. Our proposed framework is experimentally validated by formal methods using model checking tool called automated validation of internet security protocols and Scyther tools. Security analysis shows that the proposed mechanism works better compared to existing SMS payment protocols for real-world applications.

Mobile devices are becoming targets for hackers and malicious users due to the multifold increase in its capabilities and usage. Security threats are more prominent in mobile payment and mobile banking applications (MBAs). As these MBAs, store, transmit and access sensitive and confidential information, so utmost priority should be given to secure MBAs. In this paper, we have analyzed MBAs of several banks running on two dominant platforms of Android & iOS using both static and dynamic analysis. We have proposed threat model, to detect various vulnerabilities rigorously. We have done a systematic investigation of different unknown vulnerabilities particularly in mobile banking applications and showed how MBAs are vulnerable to MitM attacks. We observe that some MBAs are using simple HTTP protocol to transfer user data without concerning about security requirements. In Most of the cases, MBAs are receiving the fake or self-signed certificates. These are blindly maintaining all certificates as sound and valid, which leads to SSL/TLS Man-in-the-Middle (MitM) attacks. We present a detailed analysis of the security of MBAs which will be useful for application developers, security testers, researchers, bankers and bank customers.

Mobile banking activity plays a major role for M-Commerce (Mobile-Commerce) applications in our daily life. With the increasing usage on mobile phones, vulnerabilities against these devices raised exponentially. The privacy and security of confidential financial data is one of the major issues in mobile devices. Android is the most popular operating system, not only to users but also for companies and vendors or (developers in android) of all kinds. Of course, because of this reason, it’s also become quite popular to malicious adversaries. For this, mobile security and risk assessment specialists and security engineers are in high demand. In this paper, we propose STAMBA (Security Testing for Android Mobile Banking Apps) and demonstrate tools at different levels. These supported tools are used to find threats at a mobile application code level, communication or network level, and at a device level.We give a detailed discussion about vulnerabilities that help design for further app development and a detailed automated security testing for mobile banking applications.

Emerging M-Commerce (Mobile-Commerce) activity opened scope for the design of new protocols. Now a days SMS (Short Message Service) as a mobile payment channel is gaining prominence because it supports low end devices and low of cost. In this paper we proposed a novel protocol for SMS based mobile banking and payment framework with formal verification. The framework of A Secure SMS-based Mobile Banking Protocol with Formal Verification (SSMBP) involves authentication and payment. This model provides secure SMS communication between the customer and the bank through a mobile phone banking application. Further, its verified that the protocol for its correctness and security properties because most of the protocols lack of achieving them. Our proposed framework is experimentally validated by a formal method of model checking tool called Automated Validation of Internet Security Protocols and Applications (AVISPA) and Burrows Abadi and Needham logic (BAN). Security analyses shows that, the proposed mechanism works better, compared with existing SMS payment protocols for real-world applications.