Aug 2026· Journal of Advanced Science and Optimization Research· 0 citations
TL;DR
The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs.
Abstract
The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
The proposed BlockSafeNet framework achieved significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures, providing a positive impact on the SC ecosystem.
The smart grid systems are increasingly becoming digital where the system is taking a new direction into greater operational efficiency and real time energy management. The increased number of Internet of Things (IoT) devices and communication networks has widened the area of attack as well, thus smart grids are vulnerable to more sophisticated cyber attacks like false data injection, denial-of-data manipulation and service attacks. The traditional centralized security controls are not sufficient because of the limitation of single point failure, scalability limitations, and demand decentralized and resilient security controls. The use of blockchain technology in this regard has become a potential solution in order to improve the level of security by utilizing the transparent, decentralized, and immutable features of the technology. To provide cybersecurity approaches of smart grid systems, a taxonomical structure of blockchain technology is proposed in this paper. Besides that it shows the categorization of the various techniques into secure data management, secure energy trading, device authentication, data integrity assurance and communication protection. A detailed analysis of existing works was undertaken to determine their efficiency based on the security, scale and complexity of implementation. The results of the review describe some of the major challenges, such as latency and energy overhead and provide future research opportunities on effective smart grid security frameworks.
F. Basheer, Hari Gobind Pathak, Meena Malik et al.· International Conference on...· 0 citations
In the digital era, the prevalence of cyber threats within cloud-based infrastructures presents a formidable challenge. This study introduces a novel approach that combines the immutable nature of blockchain technology with advanced detection mechanisms to enhance the security of cloud environments. We propose a model that leverages the synergy of blockchain's distributed ledger capabilities and cutting-edge intrusion detection systems (IDS) to establish a dynamic and decentralized framework for cyber-attack detection and prevention. Our innovative method involves a multi-layered detection algorithm that operates in conjunction with a blockchain network to make sure the data integrity and veracity of application transmissions. With integration, the proposed system not only detects but also systematically records cyber attack patterns, thereby creating a robust database of digital signatures that can be used for future prevention measures. This proactive approach ensures a swift and secure method of identifying potential threats, which will significantly reduce the risk of data breaches along with system infiltrations. The implementation of this method is anticipated to provide a reliable and transparent mechanism for safeguarding sensitive information stored within cloud services. It advances cybersecurity, protecting service providers and end-users from changing cyber threats.
Eruguralla SatishBabu, Smitha Chowdary· International Conference Com...· 0 citations
This WSNs coupled with the Internet of Things (IoT) is very much needed in facilitating smart environment like smart cities, industrial automation, healthcare monitoring and environmental monitoring. Nevertheless, WSN-IoT systems are extremely susceptible to security risks such as denial-of-service attacks, malicious node behaviors, manipulation of routing, data manipulation, and privacy breaches due to their distributed, heterogeneous, and resource-constrained nature. Conventional centralized security models are usually insufficient to deal with these dynamic and scale cyber threats. The paper describes a detailed overview of blockchain-based and artificial intelligence (AI)-based security solutions to the distributed WSN-IoT scenarios. The paper examines the security dangers at varying layers of a IoT architecture and surveys the recent frameworks that incorporate machine learning, deep learning, and federated learning along with blockchain technology in managing decentralized trust and identify intrusions. Moreover, performance trade-offs on the effectiveness of security, energy use, latency, and scalability are discussed. Lastly, the research indicates the presence of an open challenge and future research topics of creating secure, scalable, and intelligent WSN-IoT infrastructure. The paper also suggests a solution of integrative AI-blockchain security framework that would have a combination of AI-based intrusion detection and decentralized blockchain trust management.
S. Madhuri, D. Dhevi· International Conference Com...· 0 citations
