The More the Merrier: Adding Hidden Measurements for Anomaly Detection and Mitigation in Industrial Control Systems
Jairo Giraldo, CheeYee Tang, David Urbina, Alvaro A. Cardenas
Industrial Control Systems (ICS) collect information from a variety of sensors throughout the process, and then use that information to control some physical quantities. Control engineers usually have to pick which measurements they are going to use and then they purchase sensors to take these measurements; however, in most cases they only need a small subset of all possible measurements that can be used. Economic and efficiency reasons motivate engineers to use only a small number of sensors for controlling a system; however, as attacks against industrial systems continue to increase, we need to study a systematic way to add sensors to the system to identify potentially malicious attacks. We propose the addition of hidden sensor measurements to a system to improve its security. Hidden sensor measurements are by our definition measurements that were not considered in the original design of the system, and are not used for any operational reason. We only add them to improve the security of the system and using them in anomaly detection and mitigation. We show the addition of these new, independent, but correlated measurements to the system makes it harder for adversaries to launch false-data injection stealthy attacks and even if they do, it is possible to limit the impact caused by those attacks. When an attack is detected we replace the compromised sensor measurements with estimated ones from the new sensors improving the risky open-loop simulations proposed by previous work.
Hot Topics in the Science of Security (HotSoS) 2020
April 7-8, 2020
Lawrence, KS, US
Cybersecurity, Cyber-physical systems (CPS), Industrial control systems (ICS)
, Tang, C.
, Urbina, D.
and Cardenas, A.
The More the Merrier: Adding Hidden Measurements for Anomaly Detection and Mitigation in Industrial Control Systems, Hot Topics in the Science of Security (HotSoS) 2020, Lawrence, KS, US
(Accessed May 30, 2023)