Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection
This work addresses cybersecurity threats in smart grids, but it is incremental as it applies existing machine learning methods with feature selection to a known problem.
The paper tackled the problem of detecting stealthy False Data Injection (FDI) attacks in smart grids, which current systems cannot handle, by analyzing three supervised learning techniques combined with three heuristic feature selection methods, resulting in improved classification accuracy across IEEE bus systems.
False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.