  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.
