Enhancing IoT-Enabled Cyber-Physical Systems with Cyber-AttackDetection and Attribution
Dhanasekaran Ranjith, Ms. Rabiya Praveen · Journal of engineering sciences. · 2024
Cyber-Physical Systems (CPS) enabled by the Internet of Things (IoT) present unique security challenges as security solutions designed for traditional IT/OT systems may not be sufficient in CPS environments. Therefore, in this study, we introduce a two-stage ensemble framework for attack detection and attribution suitable for CPS, specifically industrial control systems (ICS). To identify attacks in imbalanced ICS environments, decision trees are combined with a unique ensemble model for deep representation learning. In a next step, an ensemble of deep neural networks is used to support attack attribution. Datasets from a gas pipeline and a water treatment system are used to practically test the proposed model. The results show that the proposed model performs better than competing methods with the same computational complexity.