Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abstract

The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research.