A Deep Learning-Based Attack Detection Mechanism Against Potential Cascading Failure Induced by Load Redistribution Attacks

Abstract

The occurrence of load redistribution (LR) attacks has disastrous consequences for the power system, but these attacks have a significant impact when they cause cascading failures in the system. The mechanisms and strategies for detecting and designing LR attacks resulting in overflow on lines have been the focus of different studies. But fewer studies have been done to detect LR attacks that cause cascading failures. This paper presents a mechanism for identifying LR attacks that, besides causing overflow on lines, have the potential to generate cascading failure. A novel LR attack scheme with cascading failure potential is first proposed for this purpose. The detection mechanism has a basic exploitable structure based on analyzing the estimated cyber load data through the energy management system and a deep learning network. The performance evaluation of the detection mechanism is implemented with regard to the IEEE standard 118-bus system. Various attack scenarios under different conditions (topologies, target lines, and attack load level variations (α)) are used to verify the effectiveness of the proposed framework. The results clearly show an acceptable level of accuracy for the proposed mechanism, which can distinguish between LR attacks via the overload purpose while also showing the system’s secure state.