<![CDATA[The digital transformation of critical infrastructure, particularly Smart Grid and SCADA systems, has exposed new vulnerabilities to complex cyber-attacks such as False Data Injection (FDI), necessitating proactive defense mechanisms that transcend conventional approaches. Through a Systematic Literature Review (SLR) of 51 state-of-the-art studies (2022–2026), this research confirms a paradigm shift from static Deep Learning models toward adaptive, transparent, and decentralized detection ecosystems. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns and LightGBM for edge computing efficiency. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns (yielding up to 99.81% detection accuracy) and LightGBM for edge computing efficiency (reducing operational latency to under 10 ms). The superiority of H-ASICS is further reinforced by the integration of Explainable AI (XAI) and blockchain technology to guarantee the transparency of mitigation decisions and the immutability of cyber forensic data. This proposed architecture provides a strategic roadmap for next-generation security systems that are not only accurate and resilient but also highly accountable.]]>
Copyrights © 2026
