Nan Geng, Can Zhou, Jiafeng Feng, Xin Zhang · 7 authors
The advancing integration of Cyber-Physical-Social Systems (CPSS) within the modern power industry has highlighted the need for enhanced data integrity and multi-entity coordination. In this context, the pursuit of secure and trustworthy lifecycle management for power materials, regarded as a foundational component in ensuring system stability and operational efficiency, has attracted increasing attention. However, existing systems often face limitations such as information opacity, insufficient data accuracy, and the absence of a secure trust mechanism, hindering intelligent development and long-term sustainability. Blockchain technology, distinguished by its distributed ledger, transparency, immutability, and smart contract capabilities, offers a promising solution by enhancing data security and ensuring information reliability. This study introduces a blockchain-based framework for the secure and trustworthy lifecycle management of power materials within CPSS environments, which ensures lifecycle traceability, real-time monitoring, and trustworthy information exchange. By integrating key application scenarios, such as refined equipment management and paperless execution of contracts, the proposed approach addresses crucial operational needs. A multidimensional analysis with conventional systems reveals its advantages in improving management efficiency, optimizing resource allocation, enhancing data security, and reducing operational costs. The proposed framework thus provides both theoretical foundations and practical pathways for leveraging blockchain in power material lifecycle management, enabling digital transformation, managerial innovation, and collaborative industry development.
Energy saving is need of hour and effective energy management for Smart grids is no exception. Efficient management of Smart grids is complex task and thus needs state-of-the-art technologies for efficient management and ensuring data privacy. For secured and efficient management the emerging technologies like Federated Learning and Blockchains can be deployed. The integration of Federated learning and Blockchain offers a promising solution for these advanced and decentralized energy management systems. Federated Learning is distributed machine learning approach where the model is trained over multiple decentralized devices. data centralization. Blockchain technology offers, immutable,secured distributed ledger system that complement the Federated Learning framework. The integration of Federated learning and Blockchain facilitates secure tamper proof data analytics, which can transform energy management systems. The present research paper proposes a model for Smart grids which is based on integration of FL and BC technologies. The study discusses System Architecture for FL-BC framework. Study also proposes the possible simulation for real-world smart grid scenario consisting of smart energy devices like smart meters, solar panels, industrial IoT sensors, and Home Energy Management Systems (HEMS). It can be simulated using Python with the help of machine learning libraries like TensorFlow, PyTorch.
With the deep integration of 6G, the Internet of Things, and artificial intelligence, this paper proposes an intrusion detection and defense framework that combines robust AI kernel reconstruction, a cross-layer collaborative perception architecture, and a dynamic defense closed-loop mechanism to address advanced persistent threats and dynamically evolving attacks targeting next-generation consumer services. First, a lightweight detection model ATF-KDBC is designed based on adversarial training and online knowledge distillation. Gradient masking and noise injection are employed to enhance robustness against adversarial samples, while a drift-aware module enables adaptive optimization under concept drift scenarios. The model achieves accuracies of 99.25% and 99.84% on the NSL-KDD and IoT-23 hybrid datasets, respectively, and compresses the model size to 1.08 MB, representing a 97.6% reduction compared with the BERT teacher model. Second, a multidimensional attack chain analysis model is developed based on a STHGN. By integrating semantic, structural, and temporal features with a multi-head self-attention mechanism, the model enables cross-layer threat tracing and millisecond-level response, achieving an F1-score exceeding 97.0% on the DARPA dataset. Furthermore, this study explores the construction of a distributed CTIS network by integrating federated learning and blockchain technology. Zero-knowledge proofs are employed to ensure privacy preservation, while a Quality of Data and Quality of Model scoring mechanism enables efficient and precise deployment of defense strategies. Experimental results demonstrate that the proposed framework significantly outperforms traditional methods in terms of robustness, environmental adaptability, and computational efficiency, thereby providing both theoretical support and a technical pathway for enhancing the resilience and security of next-generation consumer services.
Daojing He, Ding Ke, Sammy Chan, KimâKwang Raymond Choo
Smart contracts have been the target of attackers (e.g., identifying and exploiting vulnerabilities). Existing countermeasures for detecting threats in smart contracts include symbolic execution, formal verification, and fuzzing, most of which only target specific known threats. However, such approaches may not be effective in detecting unknown/unseen threats (e.g., those without predefined vulnerability patterns). Building on the principles of smart contract threats and the immutability property, we propose a path profiling-based threat detection (PPTD) approach. To achieve accurate tracking of cyclic and acyclic paths, PPTD combines the profiling all paths (PAP) algorithm with the efficient path profiling (EPP) algorithm to record contract execution paths. This incurs lower gas overhead while effectively detecting and preventing threats. PPTD obtains legal paths and achieves data flow level detection through fuzzer, and automatically protects vulnerable smart contracts from threats, avoiding manual modification of vulnerable codes. Specifically, our approach is also designed to detect threats and prevent attacks after the contract is deployed, as demonstrated in our evaluations.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.
The rapid progress of quantum computing poses significant challenges to traditional cryptographic mechanisms, necessitating the adoption of post-quantum cryptography (PQC) solutions. This paper proposes a Quantum-Enhanced Security for Smart Meters (QESM) system to protect power plant data in smart cities, integrating Kyber for secure key exchange, FALCON (Fast-Fourier Transform over Lattice-based Cryptography) for quantum-resistant digital signatures, and ZKP (Zero-Knowledge Proof) for effective verification without revealing sensitive data to secure power plant data against quantum attacks. To evaluate the security of the proposed system, we analyze its resistance to various quantum threats, including Shorâs algorithm, Groverâs algorithm, quantum key analysis, quantum reversal encryption, quantum amplification, quantum switching, and quantum collision attacks. In the current study, accurate measures were used and the average was approximately 7.065 (bits/byte) for randomness, the average execution time was 6.202 milliseconds, the average memory consumption was approximately 4.343 KB, 6.4 Completeness was equal to 1 and unforgeability was 100%. As for the average throughput, it was approximately 485,605 operations per second. That shows the QESM system provides strong security and efficiency, making it a viable solution for protecting the electricity infrastructure in smart cities in the quantum era.
The emergence of 6G-connected smart cities introduces unprecedented challenges in ensuring security, privacy, and scalability for billions of heterogeneous devices and mission-critical services. Traditional Zero Trust Architectures (ZTA) provide continuous verification but rely on centralized control, making them vulnerable to insider threats and single points of failure. Conversely, blockchain-based frameworks ensure immutability and decentralized trust but suffer from high latency and limited scalability. This paper proposes a novel Blockchain-Enabled Zero Trust Architecture (BZTA) that integrates blockchainâs distributed trust management with Zero Trustâs continuous authentication and micro-segmentation, optimized for 6G urban infrastructures.The contributions of this work are fourfold. First, we design a four-layer BZTA model incorporating decentralized identity management, Zero Trust Gateways (ZTGs), blockchain-based ledgers, and smart contracts for adaptive access control. Second, we formalize the methodological foundations of the framework, including trust computation, Zero-Knowledge Proof (ZKP)-based authentication, authorization logic, and Proof-of-Authority consensus mechanisms. Third, we present a comprehensive evaluation using analytical models and simulations. Results show that BZTA achieves 95% trust classification accuracy, sub-20 ms authentication latency compliant with 6G URLLC, revocation within 3 s, and throughput up to 50,000 transactions per second, while reducing authentication energy costs by 35â40% compared to blockchain-only systems. Fourth, we demonstrate that BZTA provides robust defense against spoofing, replay, insider, lateral, and location spoofing attacks, significantly outperforming both ZTA-only and blockchain-only approaches.The findings highlight BZTA as a scalable, resilient, and privacy-preserving security paradigm for 6G smart cities. By merging blockchain immutability with Zero Trustâs dynamic verification, BZTA enables secure and transparent deployment of critical urban services such as healthcare, transportation, and energy management. This work positions BZTA as a foundational step toward building resilient, citizen-centric, and quantum-ready smart city infrastructures.
The increasing reliance on smart grids to manage power distribution efficiently has introduced significant cybersecurity vulnerabilities due to their interconnected nature. Traditional security approaches often fall short in real-time protection, particularly against advanced threats such as data manipulation, unauthorized access, and Distributed Denial-of-Service (DDoS) attacks. This paper proposes a novel Smart Grid Secure Protocol (SGSP), integrating Attribute-Based Zero-Knowledge Proofs (AB-ZKP), Redundant Consensus Mechanisms combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) for scalable and fault-tolerant consensus, and Grid Safe Smart Contracts (GSSC) to enhance data confidentiality, automate security enforcement, and resist cyber threats. The AB-ZKP mechanism ensures selective attribute verification while preserving privacy and keeping sensitive data off-chain. The hybrid consensus mechanism merges energy-efficient PoS with fault-tolerant PBFT, securing the Blockchain layer against DDoS and Sybil attacks. Meanwhile, GSSCs automate transaction validation and policy enforcement, reducing human intervention and enabling real-time anomaly detection. Experimental results in a simulated environment demonstrate high resilience, improved data privacy (98.7% compliance), fast consensus (1.2 s), low energy consumption (0.09 kWh/transaction), and strong DDoS resistance (92.5/100). The proposed approach significantly outperforms traditional methods, paving the way for secure, scalable, and privacy-preserving smart grid ecosystems.
Collin Arnold Kabwama, Osorachukwu Maurice Ayozie, Justin Njimgou Zeyeum, Adeniran Oluwatoyosi Awe · 6 authors
As Critical National Infrastructure (CNI) becomes increasingly digitized, traditional reactive security assessments are failing to keep pace with automated threats. This paper proposes an autonomous framework integrating Generative Adversarial Networks (GANs) and Distributed Ledger Technology (DLT) for proactive vulnerability discovery and immutable infrastructure hardening. We utilize a Physics-Aware Wasserstein GAN to synthesize protocol-specific attack vectors that identify "zero-day" weaknesses in Industrial Control Systems (ICS) by exploring the operational state space of protocols like DNP3 and Modbus. Discovered vulnerabilities are committed to an Immutable Knowledge Base (IKB) on a sharded, permissioned blockchain, providing a "Single Source of Truth" for cross-sector threat intelligence. To automate mitigation, Smart Contracts orchestrate infrastructure hardening by validating patches through digital twin simulations before network-wide deployment. Experimental results using HELICS and NS-3 demonstrate that this architecture reduces the Mean Time to Remediate (MTTR) from days to sub-second intervals. Finally, we address long-term security by incorporating Post-Quantum Cryptography (PQC) to protect the ledger against emerging quantum threats.
Open access
Smart Grid Security and Resilience
Software-Defined Networks and 5G
Infrastructure Resilience and Vulnerability Analysis
Securing data in the Industrial Internet of Things (IIoT) is critical due to the growing complexity and scale of industrial networks. Integrating Ethereum-based blockchain technology offers a promising solution by leveraging distributed ledgers to enhance the transparency, immutability, and security of IIoT systems. This study aims to enhance threat detection and data protection in IIoT environments through blockchain integration. To achieve this, the proposed approach incorporates Dynamic Threat Landscape (DTL)-based Intrusion Detection Systems (IDS) for real-time attack modelling, enabling systems to adapt to evolving threats. However, integrating blockchain with IIoT also presents challenges, including ensuring low latency for real-time processing, scalability to manage large volumes of sensor data, and maintaining robust cybersecurity while preserving data privacy and integrity. Addressing these concerns is essential for the effective deployment of blockchain-enabled threat detection in IIoT networks.
With the growing demand on cutting edge technologies specially in the field of internet of things (IoT) cybersecurity the intersections between blockchain and federated learning (FL) is a promising research field. Blockchain based systems, which are inherently decentralized and trustless, allow local immutable logging for transparent, tamperproof security frameworks that support automated threat response via smart contracts while really being able to understand the integrity of the data being shared across a distributed network of machines. FL represents a powerful new paradigm, allowing organizations to create an accurate intrusion detection system (IDS) without needing to centralize sensitive data, a fundamental problem to solve in healthcare, finance, or Industrial IoT. Both technologies have important synergies within hybrid architectures that offer a balanced alternative by leveraging the privacy of FL and the security and auditability of blockchain to create a viable path to robust, scalable, and reliable cyber defense. There remain numerous challenges yet to be resolved around scalability, interoperability between devices and legacy systems, real-world deployment, and energy efficiency, but the cybersecurity landscape appears to be rapidly evolving to incorporate decentralized technologies. This survey material has highlighted the areas in which progress is being made, outlined the core strengths and limitations of the technologies and approaches employed today, and suggested innovative ideas to pursue in shaping secure, and privacy-conscience, adaptive technologies in forthcoming generation of distributed and networked environments.
Bitcoin mining business has recently gained attention, especially after reports of electricity theft within MEA area for Bitcoin mining purposes. This issue has become a major concern for the MEA distribution system. Itâs estimated that non-technical losses (electricity thief) in the MEA distribution system have increased by 0.20-0.30 percent, with annual losses 8 to 15 million USD in revenue. This impact underscores the urgent need for a deeper examination of Bitcoin mining as a new form of investment, raising questions about crypto currencies and profitability for Bitcoin mining business. Illegal bitcoin mining units have diverted significant electricity through illicit connections and mining equipment by adapting meter and cable line through MEA work area and do not pay power charges. Adapted electrical system that are not up to standard can lead to short circuit, Fires, and electric shocks. This instability can harm both the lives and properly of electricity users. This abstract are delves on landscape of risks by Illegal Bitcoin mining units activities, highlighting the challenges they present to regulatory compliance and organizational resilience. Strategies to mitigate these risks and enhance operation control are also discussed.MEA has conducted a risk assessment of the above case according to the COSO ERM Framework. This assessment was carried out through a high-level management meeting and has escalated the risk to corporate level that requires urgent management action to prevent recurrence. The approach have five key actions : 1) Identify electricity theft methods by using "ArcGIS Collector" application to pin suspected location. 2) Assess the damages. 3) Coordinate with relevant agencies for legal action. 4) Develop preventive and corrective measure by install Online Load Monitoring (OLM) 5) Report to the executive. This approach ensures a comprehensive handling of electricity theft incidents, integrating detection, assessment, legal action, prevention, and improvement measures within risk management framework
Intelligent infrastructures require control strategies that overcome the limitations of centralized management. This study critically reviews the 2015â2025 literature on the convergence between bioinspired algorithms and distributed decision-making, highlighting their capabilities in decentralization, self-organization, and resilience. Based on an analysis of numerous representative cases, it is evident that Swarm Intelligence and Differential Evolution achieve significant improvements in efficiency and notable reductions in decision latency in power and traffic networks; Artificial Immune Systems strengthen the cybersecurity of critical infrastructures. Challenges remain in scalability, explainability, and ethical governance, which we address with an agenda based on federated Digital Twins and new bioinspired consensus protocols. We conclude that bioinspired algorithms not only optimize daily operations but also enable infrastructures capable of learning and recovery, laying the foundation for more sustainable and user-centered urban services.
The integration of Industrial Automation Systems (IAS) with the Internet of Things (IoT) under Industry 4.0 has significantly enhanced operational efficiency but also exposed critical communication infrastructures to cyber threats. Conventional security frameworks often fail to ensure end-to-end data integrity, authentication, and confidentiality in real-time industrial networks. This paper proposes a blockchain-enabled mathematical cryptography model designed to secure data transmission between industrial nodes. The framework utilizes Elliptic Curve Cryptography (ECC) for lightweight key generation, SHA-3 hashing for immutable transaction records, and smart contract-based consensus for autonomous trust management within a distributed ledger. A simulated industrial environment demonstrates that the proposed model achieves 42% faster encryption-decryption cycles and a 38% reduction in data latency compared to traditional asymmetric cryptosystems. The mathematical foundation ensures provable security under discrete logarithm assumptions, while blockchain consensus guarantees tamper resistance and auditability. This study contributes a scalable, mathematically robust architecture for secure data transmission in automation networks, offering potential integration within Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) environments.
Open access
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
Bhabendu Kumar Mohanta, Ali Ismail Awad, Tarek Elsaka, Hamza Kheddar · 5 authors
Intelligent devices with embedded technology have proliferated dramatically over the past decade. The Internet of Things (IoT) has emerged as a transformational force, advancing traditional systems to previously unattainable levels of intelligence. Smart cities, transportation, healthcare, supply-chain management, agriculture, water management, and smart grid (SG) systems are among the industries where the IoT has found applications. These developments are demonstrated by the integration of IoT systems into SG networks, offering significant improvements in sustainability, dependability, and efficiency. Such systems use various IoT devices to continuously monitor the environment and transmit data for processing and analysis. Nonetheless, the growth of the IoT has introduced security vulnerabilities, including concerns about user identification, data integrity, and trust, especially in SG applications. This study aims to resolve several security challenges in IoT-enabled SG applications to support sustainability. The proposed scheme effectively tackles critical security requirements such as data integrity, user anonymity, distributed storage, trust management, and decentralized architecture. The security concerns addressed by blockchain technology include preserving data integrity, fostering trust, providing secure communication, and enabling effective monitoring. Smart contracts automate system processes and are effective in maintaining user trust. The experimental findings support the viability of the proposed system, demonstrating a computational cost of 3.150 ms and a communication overhead of 992 bits, both representing improvements over various existing solutions. Additionally, the deployment cost for the smart contract is found to be 5.64 USD with a writing cost of 2.89 USD, both of which are lower than the costs associated with comparable approaches.
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
The increasing decentralization of energy generation via home solar panels and microgrids necessitates safe, scalable, and autonomous peer-to-peer (P2P) energy trading systems. Conventional grid management technologies lack the adaptability and reliability necessary for decentralized contexts. This study presents the Blockchain-Enabled Energy Swarm Protocol (BESP), which combines Ethereum smart contracts with Particle Swarm Optimization (PSO) to enhance energy trade efficiency and enable the dynamic clustering of prosumers. The protocol guarantees safe, trustless communication, low-latency energy matching, and transparent transaction auditability without dependence on a central authority. The system is assessed using empirical data from the Pecan Street Dataport dataset, which includes high-resolution records of energy usage and solar output from more than 1,000 residences in Austin, Texas. Particle Swarm Optimization (PSO) was executed in MATLAB Simulink, whilst smart contracts were deployed and evaluated via Remix IDE and Ganache on a private Ethereum network. Experimental findings indicate that BESP decreases transaction latency by 35.2%, reduces communication overhead by 27.8%, and enhances energy cost efficiency by more than 60% relative to traditional P2P and centralized frameworks. These findings underscore BESP's efficacy in facilitating energy-efficient, secure, and decentralized communications inside smart grids, in accordance with future sustainable infrastructure objectives.
In response to the growing sophistication of cyber threats, traditional centralized authentication systems have become increasingly vulnerable, particularly to AI-driven attacks and large-scale system compromises. This paper proposes a novel blockchain-based authentication framework that integrates Byzantine Quorums (BQ) to enhance resilience, decentralization, and trust. The framework introduces a dynamic intersection-based verification mechanism, enabling accurate user authentication while continuously detecting and isolating malicious nodes. By leveraging the distributed and immutable nature of blockchain along with quorum-based consensus, the system significantly reduces the risks posed by Sybil, Eclipse, and double-spending attacks. Comparative analysis with existing frameworks demonstrates superior performance in terms of security, efficiency, and malicious node detection. The proposed solution holds strong potential for deployment in critical sectors such as finance, healthcare, and energy, where secure and scalable authentication is essential.
Mohammad Aljaâafreh, Sarah Tarawneh, Hikmat Adhami, Ali Karime · 5 authors
The metaverseâa persistent, multiuser fusion of digitally augmented reality and computer-generated virtualityâ is emerging as a programmable substrate for identity, assets, and interaction. Its heterogeneous stack (XR clients, engines/SDKs, Web3 rails, wallets, marketplaces) enlarges the attack surface. This paper contributes: (i) a structured threat taxonomy specialized for Web3/XR platforms; (ii) explicit system and adversary models; (iii) a risk quantification scheme combining behavioral and on-chain signals; and (iv) a data-driven defense architecture aligning decentralized identity, wallet/custody guardrails, analytics, AI-aided detection, and policy instrumentation. We further instantiate these controls in the Medical MeTAI context, where confidentiality, integrity, and provenance requirements are stringent.
Aiming at the problems of single-point failure, privacy leakage, and high communication delay existing in the process of massive intelligent terminals accessing the new power system with traditional centralized identity authentication methods, this paper proposes an efficient identity authentication method for power terminals based on blockchain and non-interactive zero-knowledge proof. By improving Schnorr protocol, a dynamic random number driven non interactive authentication mechanism is designed to avoid the high delay of private key transmission and multi round communication. At the same time, in combination with the distributed ledger characteristics of the blockchain, the terminal public key and authentication records are decentralized stored, eliminating the dependence on a single CA. In this paper, we propose an aggregate signature method, which aggregates the zero knowledge proofs of multiple devices into a total signature, reducing the computation and communication overhead when authenticating a large number of terminal devices. This paper analyzes the performance of the proposed method using building simulation blockchain on the Hyperledger Fabric platform. Compared with other methods, this method performs well in the actual authentication phase, and reduces the time cost by more than 4.5%. Through batch certification test, compared with single terminal certification, the time cost is reduced by more than 80%. The security analysis results show that this method can resist replay attacks, phishing attacks, etc., and ensure the identity anonymity of terminal devices, the confidentiality of private keys, and the integrity of data transmission.