ReAL: A New ResNet-ALSTM Based Intrusion Detection System for the Internet of Energy

Abstract

The Internet of energy (IoE), envisioned to be a promising paradigm of the Internet of things (IoT), is characterized by the deep integration of various distributed energy systems. However, the fusion of heterogeneous IoE communication networks creates a new threat landscape. To thwart and mitigate various types of cyber threats to IoE networks, this paper proposes a novel intrusion detection system (IDS) based on a designed residual network with attention long short term memory (ReAL). Specifically, we design a light gradient boosting machine (LightGBM)-based feature selection method to identify the most useful features. Then, a residual network (ResNet) and a long short term memory neural network with an attention mechanism (ALSTM) are employed, to extract temporal patterns of network traffic events. After that, these patterns are orchestrated to identify the anomalies in IoE networks. The high effectiveness of the proposed IDS is validated on a real IoE dataset.