Publications

High-mobility Internet of Things (IoT) for vehicle-to-grid (V2G) demand response (DR), including roaming between charge point operators (CPOs), requires privacy-preserving authentication with sub-1-ms responses and cross-domain scalability as devices exceed 200 km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes reauthenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a zero-knowledge proof-based authentication scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latency L < 1 ms. ZKPAS integrates four elements: 1) Schnorr-style zero-knowledge (ZK) proofs over elliptic curves validate identity without exposing trajectory or long-term identifiers; 2) sliding-window tokens bind to DR windows and reduce per-segment communication from O(n) to O(log n); 3) predictive token generation with long short-term memory (LSTM) models trained on GeoLife and T-Drive precomputes material, yielding 84.7% token reuse along trajectories; and 4) cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the computational Diffie–Hellman problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2^(-λ). On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5% while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250 km/h.

Remote cardiac monitoring is moving from occasional clinic visits to continuous observation at home. In a typical Intelligent Internet of Medical Things workflow a wearable ECG sensor sends readings through an edge gateway to a clinical platform for storage and review. That convenience also widens the attack surface. If a cloned device is admitted or telemetry is altered clinicians may rely on false signals and patient privacy may be exposed. A longer horizon problem is also emerging. Adversaries can store encrypted medical traffic today and target it later with stronger quantum tools. At the same time many lightweight schemes still rely on static Physical Unclonable Functions whose challenge response behavior may be learned from repeated observations. We address both concerns with a compact authentication fabric for real time cardiac monitoring. The design uses a reconfigurable One Time Physical Unclonable Function so each successful session refreshes response behavior and weakens modeling attacks. It also combines ML-KEM-768 for key establishment with ML-DSA-65 for device authentication. An Isolation Forest ensemble at the gateway then screens behavioral drift that may indicate misuse or compromise. Our prototype completes authentication in 14.7 ms with about 4.6 kB per session and reaches 94.5% precision with a 2.3% false positive rate.

Cross-domain Vehicle-to-Grid (V2G) networks enable secure and efficient energy transactions between Electric Vehicles (EVs), Charging Stations (CS), and Electric Service Providers (ESP), forming a critical component of Edge computing-assisted Consumer devices and IoT (EACI) frameworks. However, existing authentication mechanisms often rely on centralized trusted authorities, leading to single points of failure and computational bottlenecks. Additionally, the emergence of quantum computing threatens traditional cryptographic authentication schemes, necessitating the integration of post-quantum security mechanisms to protect multi-domain V2G interactions. This paper presents a Lightweight Quantum-Resistant Authentication Protocol (LQAP) designed to address these challenges by employing a hybrid eXtended Merkle Signature Scheme (XMSS) and Lightweight Merkle Signature (LMS) system to achieve post-quantum resilience with optimized key management. Additionally, a decentralized Hierarchical Federated Learning (HFL) framework operating at the edge layer is incorporated for hardware-based secure entity identification, enabling distributed anomaly detection without centralized data aggregation. Additionally, physically unclonable functions (PUF) are incorporated for hardware-based secure entity identification. Formal security analysis using the Scyther tool confirms LQAP’s resilience against impersonation, replay, and machine learning-based inference attacks. Performance evaluations indicate that LQAP substantially reduces communication overhead by 21.45%, computational cost by 42.78%, and storage overhead by 61.95% compared to conventional schemes, thus providing an efficient and scalable post-quantum authentication solution aligned with EACI network requirements.

Autonomous Vehicles (AVs) represent a transformative advancement in modern transportation systems, offering significant improvements in operational efficiency and user experience. However, their widespread implementation faces critical security challenges, particularly regarding secure remote management during system failures or cyber-attacks. These vulnerabilities potentially compromise system integrity and undermine public confidence in autonomous technologies. We introduce a novel Internet of Autonomous Vehicles (IoAV) architecture integrating an AI-driven intrusion detection system with a Chaotic Map-Based Authenticated Key Agreement protocol to address these security concerns. This integration dynamically mitigates evolving security threats through adaptive system responses. Our framework incorporates Physical Unclonable Function (PUF) technology to generate cryptographically secure private keys, establishing robust communication channels between users, Charging Stations (CS), and AVs coordinated by an Electric Service Provider (ESP). Rigorous evaluation using the Real-or-Random (ROR) model demonstrates the protocol’s resilience against diverse attack vectors, including man-in-the-middle, replay, and adversarial attacks. Experimental validation confirms the framework’s effectiveness (97.8% detection accuracy, AUC-ROC: 0.976), computational efficiency (31.25% reduction in overhead, 4.2ms inference latency), and operational resilience (99.3% authentication integrity under 10³ requests/second DDoS simulation). The protocol achieves 51.38% reduced communication overhead compared to existing solutions, establishing our framework as demonstrably superior for IoAV security implementation within resource-constrained autonomous transportation infrastructures.

Autonomous vehicles (AVs) are transforming transportation systems, necessitating secure digital infrastructures for reliable operation. Vehicular Digital Twin (VDT) networks address AV limitations by enabling synchronized virtual replicas. However, intra-twin communications over public channels expose systems to severe security threats, including impersonation and data tampering. This paper proposes EDTAP-VDT: an Enhanced Distributed Trust Authentication Protocol for VDT networks, which ensures secure identity verification through a threshold cryptographic model anchored in hardware. The protocol employs a hierarchical edge-fog-cloud architecture to balance authentication loads and leverages Trusted Platform Modules (TPMs) to cryptographically bind credentials. Post-quantum secure primitives and a permissioned blockchain with Byzantine consensus ensure long-term security, pseudonymity, and immutable authentication traceability. Attribute-based access control is integrated into the authentication process for fine-grained data sharing. EDTAP-VDT guarantees confidentiality, forward secrecy, and desynchronization resilience while maintaining decentralized control. The protocol is formally validated using the Random Oracle Model, and additional resistance is demonstrated against active and passive attack vectors. Performance evaluation across realistic vehicular settings shows that EDTAP-VDT achieves up to 24% improvement in computational efficiency and up to 22% reduction in communication overhead compared to state-of-the-art alternatives while fulfilling all standard security attributes. The results establish EDTAP-VDT as a future-ready authentication framework for real-time, secure VDT applications in intelligent transportation environments.

The evolution of Smart Grids, a cornerstone of the Internet of Things (IoT), has revolutionized the electricity energy sector by enabling efficient, scalable, and secure energy management. As critical components of Smart Grid infrastructure, smart meters facilitate real-time data exchange between end users and service providers. However, transmitting sensitive data within these networks poses significant security challenges, particularly in the face of emerging quantum computing threats. Existing lightweight authentication and key exchange (AKE) protocols often fail to provide identity anonymity and impose substantial computational overhead, rendering them unsuitable for resource-constrained devices like smart meters. This paper presents a novel secure communication architecture for the Edge computing-assisted consumer devices and IoT (EACI) ecosystem, integrating Post-Quantum Cryptography (PQC) to ensure robust data integrity and access control against classical and quantum-era threats. The architecture employs a lightweight consortium blockchain, implemented using the Hyperledger Fabric framework, to maintain tamper-proof records of authentication events and energy transactions. Regional Edge Gateways (REGs) preprocess data and perform localized cryptographic operations, minimizing computational demands on resource-constrained devices. Furthermore, an AI-driven Intrusion Detection System (IDS) enhances the framework’s resilience by proactively detecting and mitigating security threats in real-time. The proposed architecture undergoes rigorous formal security analysis, demonstrating its robustness against quantum and classical cyber threats. Performance evaluations reveal a reduction of 31% in computational overhead, a 45% decrease in communication overhead, and a 15% improvement in energy efficiency compared to existing lightweight protocols, underscoring its suitability for resource-limited environments like smart meters. These findings establish the proposed architecture as a scalable, secure, and efficient solution for Smart Grids and other IoT environments, ensuring long-term data protection, system reliability, and operational sustainability in the quantum computing era.

Federated learning (FL) has emerged as a promising framework for privacy-preserving collaborative training, yet the presence of Byzantine clients poses a critical challenge for robust aggregation. Existing defenses such as FedAvg, Krum, Trimmed Mean/Median, FLTrust, and SARA exhibit significant performance drops in dynamic or mixed-attack environments. In this paper, we propose TARS, a Trust-Aware Reinforcement Selector for robust FL aggregation under adversarial and non-IID conditions. TARS leverages a trust-regularized Q learning strategy to dynamically select the optimal aggregation rule in each round, accounting for model performance and trustworthiness signals. Experimental results on MNIST and CIFAR-10 with 20% Byzantine clients demonstrate that TARS consistently outperforms all baselines, achieving a final test accuracy of 97.7% on MNIST and 80.5% on CIFAR-10, surpassing FLTrust and SARA by at least 2.5% and 3.6%, respectively, as shown in Table IV. TARS also achieves the highest optimal rule selection rate (93.6% on MNIST, 89.8% on CIFAR-10), robust convergence, and resilience against both label flipping and Gaussian attacks. These results establish TARS as a mathematically principled and empirically validated solution for trustworthy federated learning in adversarial settings.

The rapid advancement of intelligent transportation systems and the growing demand for sustainable energy solutions have elevated the Vehicle-to-Grid (V2G) paradigm in Industrial Cyber-Physical Systems (ICPS). This paper presents an AI-Enhanced Secure Protocol for V2G Energy Management, integrating Artificial Intelligence (AI) through Long Short-Term Memory (LSTM) networks with advanced cryptographic techniques for optimizing energy distribution between smart grids and electric vehicles. This protocol enhances system security and device integrity, effectively countering cyber threats and physical tampering. Emphasizing practical applicability, it demonstrates scalability and versatility across various smart grid environments, marking a significant step in AI-integrated cybersecurity for sustainable energy management. Comparative analysis reveals reductions in computation and communication costs by 49.79% and 23.24%, respectively, highlighting the efficiency of the protocol and its potential to enhance smart grid security frameworks.

The concept of the smart city and the extensive use of vehicles are evolving progressively. This evolution makes vehicular ad-hoc network (VANET) a widely used network for inter-vehicular communication to get information about speed, traffic congestion, road condition, and vehicle location. This information is obtained from a public network, which is prone to various attacks. Generally speaking, data security in VANET is one of the most crucial tasks. We found many authentication protocols for VANETs; however, most of these protocols are insecure due to impersonation, replay, denial of service attacks, etc. Moreover, the existing protocols utilized heavy computation and communication costs, making them incompatible for resource-constrained VANET applications. This article presents a lightweight privacy-preserving authentication protocol for VANETs to offer a secure and efficient data transmission mechanism over a public channel. We analyze that our protocol is lightweight, safe, and scalable compared to related protocols. The communication and computation cost of our protocol is less concerning the state-of-the-art protocols. Hence, it is overt from the observed facts that our protocol is more reliable, convenient, and practical for secure data transmission in VANETs.

Abstract Due to rapid advancement in internet technologies, remote client access has become much more comfortable than in previous days. The responsibility of the system does not limit even after providing access to resources. In every system, there is a possibility that an adversary may use the resources of the system without prior authentication. This will create interruption, and the clients of the system will ultimately be affected, so the system must provide some authentication mechanism. Therefore, an authentication scheme needs to be introduced, which provides security against different attacks. Consequently, in this article, we propose a reliable and secure three factor authentication scheme that prevents various security attacks. The provable security of the proposed scheme is proved through a widely used random oracle model. We implemented the cryptographic operations of proposed and related schemes on a desktop system having good specifications to get the experimental results. Moreover, the presented scheme is compared with the existing schemes to compare its performance. The performance evaluation shows that our scheme is lightweight and efficient in terms of computation and communication costs as compared to related competing schemes.

The Internet of Things (IoT) facilitates different devices and objects to get connected with the Internet. This connectivity helps to switch data via various emerging technologies, which shapes the vision of intelligent identification into reality. As a foremost base of the IoT, the wireless sensor networks (WSNs) have been practiced in various fields, such as smart transportation and smart healthcare. Due to rapid development of WSNs and wireless medical sensor networks (WMSNs), etc., the data security issues and challenges, such as leakage of secret data, have also gained the serious attention of the researchers. Recently, the researchers have devised various authentication protocols for WMSNs, but many of them have serious security flaws. In order to overcome the security flaws of recently designed protocols, Li et al. (IEEE Syst. J., vol. 14, no. 1, pp. 39–50, Mar. 2020) presented a three-factor user authentication protocol for WMSN. Their work claimed that their proposed protocol not only offers user anonymity but also prevents sensor node impersonation attack. In this paper, we present the detailed security analysis of Li et al.’s protocol, which shows that their protocol fails to resist sensor node impersonation attack and does not offer user anonymity. At the end, we suggest the suitable remedy to overcome the deficiencies present in Li et al.’s protocol.

In this paper, we introduce a robust authentication protocol for Vehicle-to-Grid (V2G) communication through lightweight and secure cryptographic primitives. In addition, we utilize signcrypt and unsigncrypt functions which facilitate the consumers to have secure access to the services. The security evaluation of the introduced framework shows its resilience against well-known security attacks. Moreover, the performance analysis shows that our protocol is efficient and reliable as compared to various related protocols but energy and computation cost is slightly higher than four other related protocols.

Cyber-physical-social systems (CPSSs) epitomize an evolving paradigm, including the social, physical, and cyber world. The vital goal of CPSSs is to offer personalized, high-quality, and proactive services for the end-users. An ingenious framework for reliable services is required for CPSSs to achieve this purpose. In this regard, the cloud storage environment of cloud computing (having a great connection with the physical, cyber, and social world) requires a reliable framework for secure communication between cloud and users. Cloud storage provides various services that need scalable, cost-effective, and secure facilities of data management. Public cloud storage bound its users to maintain strict security considerations that are offered by cloud service providers. On the other hand, an opportunity for users is offered by private cloud storage to construct a controlled and self-managed model of data security. This mobile model is responsible for managing the sharing and accessing of data privately. Despite that, it induces new challenges of data security. One censorious problem is to ensure the authenticated and secure model of data-storage for accessing the data under the controlled environment of data accessibility. To tackle this challenge, many protocols have been developed. The problem is that all these protocols are unable to fulfill the required security efficiency and are susceptible to various security attacks. Recently, Tiwari et al. presented an authentication scheme for data sharing and access with a biometric feature. They claimed that their scheme resists significant security attacks. However, in this article, we show that the claim of Tiwari et al. for developing a secure scheme is not valid, and their protocol is insecure against user and server impersonation attacks. Moreover, the protocol of Tiwari et al. do not provide user anonymity. Therefore, we present an enhanced, secure, and convenient scheme for data access. Besides, in order to add the flexible distribution of data that is controlled by data-owner, our protocol provides proxy re-encryption in which the cloud server utilizes the proxy re-encryption key. Then, the data-owner generates the credential token during decryption for controlling user’s accessibility. The security analysis determines that our proposed protocol resists numerous security attacks. Furthermore, performance analysis determines that our protocol has practical computation, communication, and storage costs as compared to various related protocols. Consequently, our introduced protocol achieves not only the security goals but also has performance efficiency comparable to numerous relevant protocols of cloud storage.

The blend of Internet of Things (IoT) and social networking has introduced the emerging notion of social Internet of Things, which is bringing advancements in the operation of concerned industries. There are various prevailing applications of social internet of things; smart grid is one of them. The smart grid is considered as economical robust and intuitive replacement of the conventional grid. However, smart grid experiences two significant challenges, i.e. privacy and security. This article is dedicated to resolve the privacy and security concerns for the vehicle to grid networks to facilitate their large-scale integration with smart grids. As anticipation, a vigorous key agreement protocol is introduced to achieve mutual authentication with an aided feature of user anonymity. Moreover, efficiency in terms of computation, communication and storage needs to be taken care for resource-constrained infrastructure like vehicle to grid network. We have introduced a lightweight key agreement protocol using lightweight cryptographic operations such as exclusive-OR and hash etc. This protocol is validated through a formal security model. An informal security analysis is also elaborated to present the security strength of our protocol against well-known attacks. Furthermore, we have implemented all the cryptographic operations used at trusted agent’s side on a desktop system, while the operations used at battery vehicle unit’s side are implemented on an Arduino to get the experimental results. In the end, we have presented a performance analysis to compare the performance of our protocol with related ones. This comparison highlights that our protocol is not only lightweight but also efficient in terms of communication and storage cost of related protocols.