Aug 2026· The Eastasouth Journal of Information System and Computer Science· 0 citations· 42 references
TL;DR
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.
Abstract
The rapid growth of smart digital environments, driven by cloud computing, the Internet of Things (IoT), artificial intelligence (AI), and interconnected cyber-physical systems, has significantly increased the frequency and sophistication of cyber threats. Conventional cybersecurity approaches often struggle to ensure data integrity, trust, and resilience in decentralized and highly dynamic environments, highlighting the need for more robust security frameworks. This review paper examines the role of blockchain technology in enhancing cybersecurity for smart digital ecosystems through decentralized authentication, immutable data storage, secure information sharing, and intelligent threat mitigation. The study reviews blockchain architectures, consensus mechanisms, security protocols, and their integration with emerging technologies such as AI, machine learning, edge computing, and cloud platforms. A comparative analysis of consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), is presented to evaluate their effectiveness in improving data integrity, scalability, and security. The review further analyzes hybrid blockchain-AI cybersecurity frameworks, demonstrating that integrating blockchain with AI-based intrusion detection systems significantly improves threat detection accuracy, reduces successful cyberattacks, and enhances system resilience. The paper also discusses major implementation challenges, including scalability, computational overhead, interoperability, energy consumption, and privacy preservation, which continue to hinder large-scale deployment. Finally, future research directions are identified, emphasizing explainable artificial intelligence (XAI), federated learning, zero-trust architectures, edge intelligence, and privacy-preserving blockchain solutions to strengthen next-generation cybersecurity systems. 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.
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
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.
MARBIYAT TAHIR GIDADO, Bashiru Abdulganiyu, MOHAMMED NASIR MUSA et al.· Journal of Advanced Science...· 0 citations
Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem.
The rapid evolution of Intelligent Transportation Systems (ITS) has resulted in highly connected transportation ecosystems involving vehicles, roadside infrastructure, traffic management centres, cloud platforms, edge devices, and users. Although connectivity improves traffic efficiency, road safety, and intelligent mobility, it simultaneously increases exposure to cyber threats such as data manipulation, identity spoofing, Sybil attacks, denial-of-service attacks, replay attacks, malicious node behaviour, and privacy violations. Blockchain technology provides a promising cybersecurity foundation for ITS through decentralised trust management, immutable data storage, cryptographic authentication, transparent transaction verification, and smart-contract-based access control. This paper proposes a blockchain-enabled cybersecurity framework for ITS that integrates distributed ledger technology with secure vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything communication. The framework incorporates identity management, consensus-based validation, trust evaluation, secure data sharing, intrusion detection, and privacy-preserving mechanisms. Particular emphasis is placed on lightweight consensus mechanisms and edge-assisted blockchain architectures to address the latency, computational, storage, and scalability constraints of vehicular environments. The proposed framework provides a systematic architecture for improving data integrity, authentication, accountability, privacy, and resilience against cyberattacks while supporting intelligent and safety-critical transportation services.
Dr Rashmi Soni, Dr Ved Prakash Mishra, Dr. G. Soma Sekhar et al.· Journal of Intelligent Decis...· 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
The rapid digitalization of healthcare has led to the generation of vast amounts of sensitive patient information, increasing the need for advanced security solutions beyond traditional centralized systems. This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security. Conventional electronic health record systems often face challenges such as single points of failure, limited transparency, and vulnerability to cyber threats. Blockchain addresses these issues by providing a decentralized and immutable ledger that ensures data integrity, traceability, and secure record management through cryptographic techniques and consensus protocols.
In parallel, AI strengthens security by enabling intelligent threat detection, predictive analytics, and adaptive authentication mechanisms. Machine learning algorithms continuously analyze network activities and user behaviors to identify potential breaches and insider threats in real time. The combination of AI and blockchain creates a synergistic framework in which AI enhances blockchain efficiency, while blockchain provides a transparent and trustworthy environment for AI-driven data processing.
The study further explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data. Key challenges, including interoperability, scalability, regulatory compliance, and integration with legacy systems, are also discussed. Additionally, patient empowerment is enhanced through self-sovereign identity models that grant individuals greater control over their personal health information.
Despite challenges related to computational complexity and standardization, the convergence of AI and blockchain offers a proactive, resilient, and privacy-preserving security architecture for modern healthcare. Future research should focus on lightweight cryptographic solutions, quantum-resistant security mechanisms, and governance frameworks for decentralized healthcare ecosystems. Overall, this integration represents a significant step toward secure, transparent, and patient-centered digital healthcare systems.
O. Gbolade· Journal of Information Techn...· 0 citations
