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

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.

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

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

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

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

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

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 Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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 Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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. © 2024 Elsevier Inc.

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 process. 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 IoV environment. The findings demonstrate the practicality of the proposed framework in vehicular networks, providing a reliable and trustworthy approach for IoV communication.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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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.

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.

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.

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.