Smart Grid Intrusion Detection System Based on Incremental Learning

With the rapid development of information and communication technology, the intelligent transformation process of traditional power grid continues to accelerate. As an important innovation in the field of power service, smart grid completely revolutionizes the traditional power supply process, and relies on an agile and efficient communication network to realize the two-way interaction between users and the power grid, which significantly improves the power supply flexibility and service quality. However, the two-way communication process is vulnerable to all kinds of network attacks, but most of the current intrusion detection schemes are difficult to effectively identify the emerging attack types, even if incremental learning methods are adopted, they are often trapped in catastrophic forgetting problems. In order to meet the above challenges, this paper proposes smart grid intrusion detection system (Grid-IDS). By establishing an incremental learning method based on tree structure, it can not only accurately detect existing attacks, but also incrementally learn new attack types, and at the same time relief the catastrophic forgetting problem caused by incremental learning. Experiments show 99.65% accuracy on CICIDS2017 with performance superior to baselines, and competitive accuracy and precision on WUSTL-IIoT-2018, indicating good generalization under heterogeneous traffic.