Intelligent Intrusion Detection System for Smart Grid Applications

Smart grid is a cyber-physical system that enhances the capability of conventional power networks leveraging functional automation of information and communication technologies. These systems allow energy provider companies to deliver low cost reliable power with minimal losses. Despite the advantages, such cyber-physical systems are prone to heterogeneous attacks leading to a breach of data integrity and confidentiality. A significant part of the research that aims to tackle such weaknesses of the smart grids, suggests intrusion detection systems (IDS) as an effective solution. However, robustness, accuracy, and adaptability to new attacks are the major concerns in such systems. Therefore, we proposed an intelligent intrusion detection system for smart grid networks that uses deep reinforcement learning. Our proposed IDS is robust and highly accurate with low false alarm rate. Our model is based on the novel CVAEDDQN architecture, that combines generative model along with deep reinforcement learning. Due to lack of smart grid specific datasets, we have used benchmark network-based NSL-KDD dataset and cloud specific ISOT-CID dataset. The experimental results show the effectiveness of our proposed system in terms of accuracy and false positive rate as well as network attack detection capabilities. We have also evaluated the adaptiveness of our model with changes in attack patterns against critical attack types.