Sunandan Adhikary

Ipsita Koley, Sunandan Adhikary, Soumyajit Dey

Increased dependence on networked, software based control has escalated the vulnerabilities of Cyber Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of detection systems. In the present work, we propose a Reinforcement Learning (RL) based framework which adaptively sets the parameters of such detectors based on experience learned from attack scenarios, maximizing detection rate and minimizing false alarms in the process while attempting performance preserving control actions.

Sunandan Adhikary, Ipsita Koley, Sumana Ghosh et al.

We consider the problem of provably securing a given control loop implementation in the presence of adversarial interventions on data exchange between plant and controller. Such interventions can be thwarted using continuously operating monitoring systems and also cryptographic techniques, both of which consume network and computational resources. We provide a principled approach for intentional skipping of control loop executions which may qualify as a useful control theoretic countermeasure against stealthy attacks which violate message integrity and authenticity. As is evident from our experiments, such a control theoretic counter-measure helps in lowering the cryptographic security measure overhead and resulting resource consumption in Control Area Network (CAN) based automotive CPS without compromising performance and safety.