This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three factors, Quantum-resistant feature engineering using the UNSW-NB15 dataset adapted for solar infrastructure anomalies. An enhanced Light Gradient Boosting Machine (LightGBM) classifier with blockchain-validated decision thresholds, and A cryptographic proof-of-threat (PoT) consensus mechanism for cyber attack verification. The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision, recall and F1-score, outperforming conventional intrusion detection systems (IDSs) by 12.7% in false positive reduction. The blockchain layer demonstrates a 2.4-s average block confirmation time with 256-bit SHA-3 hashing, enabling real-time threat logging in photovoltaic networks. Experimental results improve in attack traceability compared to centralized security systems, establishing new benchmarks for trustworthy anomaly detection in smart grid infrastructures. This study also compared traditional and hybrid ML based blockchian driven IDSs and attained better classification results. The proposed framework not only delivers a resilient, adaptable threat mitigation system (TMS) for Industry 4.0 solar powered infrastructure but also attains high explainability, scalability with tamper-proof logs, and remarkably exceptional ability of endurance to cyber attacks.

Enhancing the interconnection of devices and systems, the Internet of Things (IoT) is a paradigm-shifting technology. IoT security concerns are still a substantial concern despite its extraordinary advantages. This paper offers an extensive review of IoT security, emphasizing the technology’s architecture, important security elements, and common attacks. It highlights how important artificial intelligence (AI) is to bolstering IoT security, especially when it comes to addressing risks at different IoT architecture layers. We systematically examined current mitigation strategies and their effectiveness, highlighting contemporary challenges with practical solutions and case studies from a range of industries, such as healthcare, smart homes, and industrial IoT. Our results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational capability. IoT networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning capabilities. This study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT, security challenges, and AI-driven solutions.

The exponential growth of the Internet of Things (IoT) has revolutionized various domains such as healthcare, smart cities, and agriculture, generating vast volumes of data that require secure processing and storage in cloud environments. However, reliance on cloud infrastructure raises critical security challenges, particularly regarding data integrity. While existing cryptographic methods provide robust integrity verification, they impose significant computational and energy overheads on resource-constrained IoT devices, limiting their applicability in large-scale, real-time scenarios. To address these challenges, we propose the Cognitive-Based Integrity Verification Model (C-BIVM), which leverages Belief-Desire-Intention (BDI) cognitive intelligence and algebraic signatures to enable lightweight, efficient, and scalable data integrity verification. The model incorporates batch auditing, reducing resource consumption in large-scale IoT environments by approximately 35%, while achieving an accuracy of over 99.2% in detecting data corruption. C-BIVM dynamically adapts integrity checks based on real-time conditions, optimizing resource utilization by minimizing redundant operations by more than 30%. Furthermore, blind verification techniques safeguard sensitive IoT data, ensuring privacy compliance by preventing unauthorized access during integrity checks. Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40% compared to traditional bilinear pairing-based methods, making it particularly suitable for IoT-driven applications in smart cities, healthcare, and beyond. These results underscore the effectiveness of C-BIVM in delivering a secure, scalable, and resource-efficient solution tailored to the evolving needs of IoT ecosystems.