Intrusion detection systems in smart grids: A review-based study

The fast installation and rising complexity of smart grids brought immense cyber security problems with more than 50% of cyber-attacks on the energy sector now focusing on grid infrastructures, these systems have become a top priority to protect. IDSs play a vital role in detecting and containing malicious activities prior to having an effect on grid operations. This review paper offers an exhaustive examination of IDS techniques tailored for smart grid systems. The study follows the development of IDSs from conventional signature-based techniques, providing 70–80% detection rates, to sophisticated machine learning (ML) and artificial intelligence (AI)-driven techniques. These latest techniques have delivered detection rates of up to 98%, with a reduction in false positives of 30–40% in test environments. Hybrid IDS models that integrate automated learning and human knowledge have demonstrated further enhancements in adaptability and response to threats. IDS architectures are compared based on accuracy, scalability, power efficiency, and real-time response, with current systems operating at 8–10 W power for edge deployment and under 1-second latency. Moreover, the paper discusses the legal and ethical consequences of intrusion detection, with emphasis on the balance between robust security features and user privacy, especially within regulatory schemes like GDPR. Upcoming directions in IDS development are to incorporate block chain for safe and tamper-proof logging, embracing federated learning for privacy-preserving model training, and creating adaptive, multi-layer detection frameworks to improve robustness against constantly evolving and advanced cyber-physical attacks.