Exploration of Ensemble Methods for Cyber Attack Detection in Cyber-Physical Systems

Cyber-physical systems (CPS) are prevalent in critical infrastructure, industrial settings, cybersecurity, healthcare, transportation and more. As applications and benefits of CPS continues to expand in all aspects of human existence, the number of cyber attacks increases exponentially. Although there exist myriad ensemble tecqniques for cyber attack detection, identifying the most suitable one for a given dataset can be challenging. This study presents a comparative analysis of ensemble methods for detecting binary and multiclass cyber attacks in a CPS specifically a water distribution system. This research focuses on the application and efficacy of various ensemble learning techniques such as voting, bagging, boosting and stacking using the Water Distribution testbed (WDT) dataset. The results of the experiment demonstrated that Bagging Decision Trees ensemble (BAGTREE) achieved high performance both in binary and multiclass classification. BAGTREE reached a 98% accuracy level for binary classification tasks and 99% accuracy in the multiclass classification.