Semeen Rehman

The integration of vehicular networks with cloud and IoT infrastructures requires secure and lightweight authentication to ensure privacy and reliability in highly dynamic environments. This paper proposes an adaptive multi-factor authentication protocol that combines biometrics, passwords, and elliptic curve cryptography (ECC) to provide strong mutual authentication between vehicles, roadside units (RSUs), and cloud servers. A Reed–Solomon–based fuzzy extractor is incorporated to address biometric variability, ensuring stable key generation without compromising efficiency. The protocol achieves resilience against impersonation, replay, stolen credential, and man-inthe-middle attacks, supported by a formal proof under the Random Oracle Model (ROM). To validate practicality, the scheme is implemented in Python and evaluated in a realistic vehicular simulation environment using OMNeT++, SUMO, and Veins with real-world road network data. Experimental results demonstrate low authentication delay, reduced communication and computational overhead, and high scalability under varying vehicle densities, confirming its suitability for secure and realtime vehicular cloud environments.

Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s application in sensitive sectors is hindered by challenges in meeting rigorous standards like the GDPR, with traditional setups struggling to ensure compliance and maintain trust. Addressing these issues, our research introduces an innovative Zero Trust-based DFL architecture designed for GDPR compliant systems, integrating advanced security and privacy mechanisms to ensure safe and transparent cross-node data processing. Our base paper proposed the basic GDPR-Compliant DFL Architecture. Now we validate the previously proposed architecture by formally verifying it using High-Level Petri Nets (HLPNs). This Zero Trust-based framework facilitates secure, decentralized model training without direct data sharing. Furthermore, we have also implemented a case study using the MNIST and CIFAR-10 datasets to evaluate the existing approach with the proposed Zero Trust-based DFL methodology. Our experiments confirmed its effectiveness in enhancing trust, complying with GDPR, and promoting DFL adoption in privacy-sensitive areas, achieving secure, ethical Artificial Intelligence (AI) with transparent and efficient data processing.

In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. Our approach is anchored in the realization that conventional PAD models, while effective to a degree, falter in the face of novel, unseen attack vectors and complex variations. As a solution, we suggest a novel architecture where a teacher network, trained on a comprehensive dataset embodying a broad spectrum of attacks and genuine instances, distills knowledge to a student network. The student network, specifically focusing on the nuanced detection of genuine samples in target domains, leverages minimalist yet representative attack data. This methodology is enriched by incorporating facial expressions, dynamic backgrounds, and adversarially generated attack simulations, aiming to mimic the sophisticated techniques attackers might employ. Through rigorous experimentation and validation on benchmark datasets, our results manifested a substantial leap in classification accuracy, particularly for those samples that have traditionally posed a challenge. The newly proposed model, which can not only effectively outperform existing PAD solutions, but also achieve admirable flexibility and applicability to novel attack scenarios, truly demonstrates the power of the proposed teacher–student framework. This paves the way for improved security and trustworthiness in the area of face recognition systems and the deployment of biometric technologies.

Vehicles become more advanced and smarter due to advancements in technology in the modern world. Every person now a days, demand a smart vehicle due to their automobility and smart controls. This is all possible through advancements in VANET (Vehicular Adhoc Network) and the Internet of Vehicles (IoV). Vehicles in the VANET are highly connected to each other and this thing can cause security, safety, and privacy risks for the asset itself and driver also. It can become a reason of major threat. And these threats can occur due to tracing the location of the vehicle. Existing techniques like group-based shadowing schemes, obfuscation, silent periods, and mix-zone have preserved privacy of location somehow, but don’t have a good QoS and optimized efficient security. To overcome these issues, we introduced a new privacy framework, which is an improvement of the existing shadowing scheme. We proposed a computationally efficient group leader selection process based on centeredness, rule obeyed, and OBU resources, reducing overhead by 20%, integrating FL with DP to preserve data privacy without sacrificing utility, and achieving a 15% improvement in location accuracy under privacy constraints, validating the scalability and robustness of the framework through extensive simulations involving up to 300 vehicles. Group Leader is used as an optimization of the overall framework including efficiency and implementation of the scheme. This scheme increases privacy if the number of vehicles also increases, and this thing makes our scheme more scalable. This scheme overcomes the many drawbacks of existing techniques like a higher tracing ratio in shadowing schemes, totally depending on the group leader, and reduced utility of all schemes based on distances. The most important thing, the single point of failure in the group leader base shadowing scheme is overcome by using local federated learning with differential privacy. Validation results of our proposed scheme showed that it outperformed the current schemes mainly based on group leader.

Considering that smart vehicles are becoming interconnected through the Internet of Vehicles, cybersecurity threats like Distributed Denial of Service (DDoS) attacks pose a great challenge. Detection methods currently face challenges due to the complex and enormous amounts of data inherent in IoV systems. This paper presents a new approach toward improving DDoS attack detection by using the Gini index in feature selection and Federated Learning during model training. The Gini index assists in filtering out important features, hence simplifying the models for higher accuracy. FL enables decentralized training across many devices while preserving privacy and allowing scalability. The results show that the case for this approach is in detecting DDoS attacks, bringing out data confidentiality, and reducing computational load. As noted in this paper, the average accuracy of the models is 91%. Moreover, different types of DDoS attacks were identified by employing our proposed technique. Precisions achieved are as follows: DrDoS_DNS: 28.65%, DrDoS_SNMP: 28.94%, DrDoS_UDP: 9.20%, and NetBIOS: 20.61%. In this research, we foresee the potential for harvesting from integrating advanced feature selection with FL so that IoV systems can meet modern cybersecurity requirements. It also provides a robust and efficient solution for the future automotive industry. By carefully selecting only the most important data features and decentralizing the model training to devices, we reduce both time and memory usage. This makes the system much faster and lighter on resources, making it perfect for real-time IoV applications. Our approach is both effective and efficient for detecting DDoS attacks in IoV environments.

Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. However overlaid challenges affecting today’s FL systems are centered on time optimization, scalability, and security. To these challenges, this paper responds with a new solution comprising the dynamic cohort creation via smart contracts and a hierarchical blockchain approach. Firstly, the research utilizes smart contracts for the dynamic tracking of cohorts in real time and addresses a two-tier blockchain structure for safety and efficiency in storage. In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. The method we are proposing includes four practical steps that include efficient update validation and aggregation; this will enhance training time, and increase model accuracy as well as data confidentiality. The standard dataset is used to show enhanced performance and scalability which validates this method. Based on the above investigations, it could be concluded that the proposed approach improves FL efficiency and creates a new direction in the development of secure, accurate, and scalable ML. The present study indicates that the implementation of blockchain with FL fortified by GDP will establish a novel innovation between intelligent and safe Artificial Intelligence (AI) architecture for safeguarding the privacy of ML system.

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations pose major challenges in designing robust accelerators. To address this, we introduce OPTIMA, a modeling framework that aids in analyzing bitline discharge and power consumption in 6T-SRAM-based accelerators. It provides insights into limiting factors and enables fast design-space exploration of circuit configurations. Leveraging OPTIMA for in-SRAM multiplications exhibits ~100 × simulation speed-up while maintaining an RMS modeling error of 0.88 mV. Exploration yields an optimized multiplier with 1.05 pJ energy consumption per 4-bit operation and classification accuracies of 71.8% (top-1) and 90.4% (top-5) for ImageNet and 92.5% for CIFAR-10 datasets respectively when applied in quantized DNNs. To further support research and development efforts, we have made our OPTIMAptima tool flow available as open source [https://github.com/sevjaeg/optima].

Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centralized computing systems involve single points of failure. To mitigate these challenges, we have proposed a secure and incentive-based data-sharing framework for IIoT systems using blockchain technology. We leverage blockchain due to its ability to provide secure and tamper-resistant data storage and sharing as participants store their data on a distributed ledger (DL), preventing unauthorized access. A security protocol is designed that utilizes the properties of elliptic curve cryptography (ECC). Moreover, Shapley value is employed to calculate revenue and distribute it fairly. To perform the formal security evaluation, we have conducted extensive simulations using the Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther protocol simulation tools, which demonstrated that our protocol is robust against various adversarial attacks. The experimental results show that the proposed incentive distribution framework demonstrated fairness in the distribution of revenue among participants.

Spin Transfer Torque Random Access Memory (STT-RAM) is an emerging Non-Volatile Memory (NVM) technology that has garnered attention to overcome the drawbacks of conventional CMOS-based technologies. However, such technologies must be evaluated before deployment under real workloads and architecture. But there is a lack of available open-source STT-RAM-based system evaluation framework, which hampers research and experimentation and impacts the adoption of STT-RAM in a system. This paper proposes a novel, extendable STT-RAM memory controller design integrated inside the gem5 simulator. Our framework enables understanding various aspects of STT-RAM, i.e., power, delay, clock cycles, energy, and system throughput. We will open-source our HOPE framework, which will fuel research and aid in accelerating the development of future system architectures based on STT-RAM. It will also facilitate the user for further tool enhancement.

Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs.