Mohamed Azab

Quantum computing poses distinct risks to blockchain security: Shor’s algorithm threatens signatures associated with exposed public keys, while Grover’s algorithm reduces the security margin of hash-based mechanisms. This systematic mapping study examines quantum-resistant blockchain defenses and the evidence supporting their practical use. A total of 387 peer-reviewed primary studies published between 2018 and 2026 were identified through IEEE Xplore, the MDPI journal collection, and citation searching, and classified by contribution type and evidence maturity. The findings show that post-quantum cryptographic constructions dominate the literature (56.1%), followed by quantum-native designs (26.6%) and hybrid migration approaches (13.7%). Despite substantial research on these defenses, only 16.0% of the studies report real-platform laboratory experiments, and 1.3% reach public-testnet evaluation. Hybrid migration studies demonstrate greater progress toward platform-level evaluation than other defense categories, but practical validation remains limited. These findings highlight the gap between proposed quantum-resistant mechanisms and their evaluation under realistic blockchain conditions, emphasizing the need for reproducible system-level testing and migration strategies addressing interoperability, governance, and operational continuity.

The Internet of Things (IoT) revolution has profoundly transformed modern industries by embedding connected digital devices across critical sectors such as energy, pharmaceuticals, and manufacturing. Such interconnected devices together form cyber-physical systems (CPSs), complex structures that enable computation to directly control physical processes. These systems increasingly rely on automation and real-time control to enhance efficiency, productivity, and safety. However, this growing dependence on connectivity has significantly expanded the attack surface, exposing mission-critical infrastructure to sophisticated cyber threats. Among the most dangerous are zero-day vulnerabilities, which are unknown flaws in software or firmware, and zero-day design flaws, which are deeply embedded architectural weaknesses often undetectable by conventional verification methods. Traditional defense mechanisms, largely based on static rules or known threat signatures, are ill-equipped to detect these stealthy and evolving threats—especially in real-time systems, where even minor delays in detection or response can result in catastrophic outcomes. To overcome these limitations, this work presents a transformative security framework that leverages the reasoning and generative capabilities of large language models (LLMs) to construct an intelligent, redundant digital twin of the operational system. Unlike traditional replicas, the LLM-driven digital twin is logically and architecturally isolated, making it inaccessible to attackers targeting the primary system. Crucially, the twin is not a static copy: it is dynamically synthesized and continuously restructured by the LLM to ensure a heterogeneous and evolving codebase. This ensures functional alignment with the physical system while introducing deliberate software diversity, thereby increasing attacker uncertainty and resilience to shared exploits. The central hypothesis of the framework is that semantic deviations between the decisions or behaviors of the digital twin and its physical counterpart can serve as reliable indicators of anomalous activity—whether stemming from external attacks or latent design flaws. By monitoring this divergence, the system facilitates the early detection of zero-day threats and supports real-time mitigation strategies, thereby significantly strengthening the resilience of critical infrastructure. Implementation results demonstrated the effectiveness and efficiency of the proposed approach.

The digital landscape increasingly relies on data exchange, underscoring the importance of Digital Identity Management (DIM) systems. However, current models face vulnerabilities, leading to the demand for more secure, decentralized, and user-centric solutions. Self-sovereign Identity (SSI) has emerged as a promising paradigm supported by blockchain technology, granting users control over their digital identities. This article reviews existing research and commercial SSI solutions, assessing their feasibility and effectiveness. We thoroughly explore SSI’s concept, structure, and components while addressing challenges like scalability, usability, Quantum computing-based attacks and corresponding resistance mechanisms, data storage, and governance. Additionally, we highlight recent advancements and identify research gaps, suggesting future directions for enhanced DIM solutions.

Extended Reality (XR), encompassing virtual, augmented, and mixed reality systems, rapidly evolves across gaming, education, healthcare, and industry, with increasing reliance on wireless communications. However, the immersive and continuous sensing nature of XR introduces unique security and privacy challenges that remain underexplored. This paper presents a comprehensive systematic literature review (SLR) of 78 peer-reviewed studies published between 2000 and 2024, focusing on XR security within wireless environments. This thorough review analyzes multiple electronic databases, including Web of Science, IEEE Xplore, ScienceDirect, and ACM, motivated by the need to address the integration of XR into wireless communications and its unique technical and performance challenges. We categorize threats, vulnerabilities, and limitations; highlight gaps in current encryption and authentication schemes; and emphasize growing privacy risks from biometric and spatial data. A detailed taxonomy of XR-specific attack models and a multi-dimensional mapping of XR security challenges are presented. Additionally, the paper generalizes the proposed taxonomy along a reality-virtuality continuum, offering insights into how various degrees of virtual and real experiences can coexist within wireless networks. Furthermore, we propose a research agenda addressing open issues, including real-world deployment constraints, context-aware zero-trust architectures, and privacy-preserving edge intelligence. Our review reveals an urgent need for lightweight, scalable, and standardized security solutions tailored to immersive XR systems.

Legacy SCADA protocols like DNP3 are vital for smart grid communications but lack robust defenses against modern cyber threats, especially from quantum computing. Current security enhancements, such as DNP3 Secure Authentication (DNP3-SA) and its broadcast variant (DNP3-SAB), are vulnerable due to their reliance on classical cryptography and static configurations. This paper introduces a Quantum-Safe Hybrid Authentication Protocol with Moving Target Defense (MTD), which combines post-quantum ML-KEM lattice-based cryptography with classical ECC-Diffie-Hellman key exchanges to protect against both classical and quantum attacks. The protocol employs MTD to dynamically reconfigure identities and network settings, disrupting reconnaissance and persistent attacks. It includes four phases: synchronization, key agreement, key update, and MTD-driven authentication, ensuring secure, low-latency communication. Formal analysis in universal composability framework confirms mutual authentication and session key secrecy, while Scyther tool verification demonstrates resilience against adversarial exploits. Performance evaluations show efficiency comparable to DNP3-SAB. Overall, this hybrid approach significantly enhances the security of legacy SCADA systems against evolving cyber-physical threats. For the real-world deployment, to benefit wider stakeholders, we provide a handy plug-and-play (HPP) module, which is a takeaway from this research.

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms.

Quantum key distribution (QKD), a key application of quantum information technology and “one-time pad” (OTP) encryption, enables secure key exchange with information-theoretic security, meaning its security is grounded in the laws of physics rather than computational assumptions. However, in QKD networks, achieving long-distance communication often requires trusted relays to mitigate channel losses. This reliance introduces significant challenges, including vulnerabilities to compromised relays and the high costs of infrastructure, which hinder widespread deployment. To address these limitations, we propose a zero-trust spatiotemporal diversification framework for multipath–multi-key distribution. The proposed approach enhances the security of end-to-end key distribution by dynamically shuffling key exchange routes, enabling secure multipath key distribution. Furthermore, it incorporates a dynamic adaptive path recovery mechanism that leverages a recursive penalty model to identify and exclude suspicious or compromised relay nodes. To validate this framework, we conducted extensive simulations and compared its performance against established multipath QKD methods. The results demonstrate that the proposed approach achieves a 97.22% lower attack success rate with 20% attacker pervasiveness and a 91.42% reduction in the attack success rate for single key transmission. The total security percentage improves by 35% under 20% attacker pervasiveness, and security enhancement reaches 79.6% when increasing QKD pairs. Additionally, the proposed scheme exhibits an 86.04% improvement in defense against interception and nearly doubles the key distribution success rate compared to traditional methods. The results demonstrate that the proposed approach significantly improves both security robustness and efficiency, underscoring its potential to advance the practical deployment of QKD networks.

With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless communication networks. The SSFXG framework separates identity management from the service management layer typically controlled by network operators to foster interoperability functionalities with enhanced privacy and trace-resistant assurances in the NextG landscape. The proposed model relies on blockchain technology as an infrastructure to enable single-authority-free service provisioning and boost mutual trust among federated network components. Further, the SSFXG framework facilitates subscribers’ self-sovereignty over their subscription information while ensuring anonymity and enhanced privacy preservation, avoiding unnecessary network activity monitoring or tracking. Preliminary evaluations demonstrated the effectiveness and efficiency of the proposed framework, making it a promising solution for advancing secure and interoperable NextG wireless networks.

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users, IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user’s identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (Scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications.