Multimodal Intrusion Detection System for Cyber Physical Systems

Cyber-Physical Systems (CPS) are deployed to control critical infrastructure in many fields, including industry and manufacturing. In recent years, CPS have been affected by cyberattacks due to the increased connectivity of these systems to the Internet. This work aims to develop a deep learning-based Intrusion Detection System (IDS) for detecting cyberattacks on CPS using multimodal learning techniques. This thesis reports the design, implementation, and evaluation of two IDS solutions based on different deep learning networks: Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). For the first IDS, Gramian Angular Field (GAF) is used to convert CPS time-series data to images that are fed to a 3D CNN to train the attack detection classifier. The second IDS uses RNN with a multimodal attention approach for training the attack detector. Both solutions utilize CPS process data and network data to improve the attack detection accuracy. The performance of the proposed approaches is evaluated on SWaT datasets collected from a testbed that represents real world CPS. Experimental results demonstrate that both IDSs achieved improved performance and higher detection capability compared to related work