Performance Evaluation and Deployment Strategies of Deep Learning-Based IDS

The rapid advancement of digital technologies in power systems leads to change in traditional grids to smart ones with interconnection and intelligence. While this is progress in terms of efficiency, it does, however, open the grid to a growing number of advanced cyber threats. Intrusion Detection Systems (IDS) is a key line of defense that making it possible to detect or prevent these threats. This paper offers a systematic review on the development and classification of IDS and its usage in smart grid environment. It discusses the evolution of signature-based to modern deep learning-based models with better detection power for unseen and unique attacks. Different deep learning models including CNNs, RNNs, and Autoencoders are investigated to determine their performance in real-time detection of threats. The introduction of IDS in the various layers of smart grids (physical, communication and control layers) is examined to emphasise the architectural perspectives and latency issues. Moreover, the paper discusses the legal and ethical considerations of deploying surveillance systems in critical infrastructure, and emphasizes the importance of privacy-preserving schemes and regulatory law enforcement. We finally discuss emerging massive IoT developments such as federated learning, blockchain-based security logging, and edge AI as potentially promising avenues for realizing scalable, decentralized, intelligent intrusion detection in the future. This work is an essential resource for academics, utility managers, and research-and-development employees interested in ensuring the security and reliability of the second and future generations of smart grids.