Detecting and Counteracting Benign Faults and Malicious Attacks in Cyber Physical systems

שלחו לחבר
Israel Koren, University of Massachusetts at Amherst
BIU Engineering Building 1103, Room 329

The use of cyber-physical system (CPS) is rapidly expanding and many of their applications require a highly reliable and secure implementation as they control critical infrastructures or even life-critical devices. Unfortunately, current techniques for achieving high reliability and security incur high overheads. In particular, integrating countermeasures against security attacks is problematic as security threats are often not well defined, evolve continuously, and as a result, many CPSs often remain vulnerable.  We propose to exploits the physical plant state information to enhance both reliability and security. Our approach, which monitors the controlled plant state trajectory, allows for tunable fault-tolerance as well as detection of malicious attacks, and it achieves these at a low overhead. The plant state space consists of safe and marginal state subspaces. In the safe subspace the CPS will continue its safe operation even if the worst case control signal is applied. In contrast, any erroneous control applied when the plant state is marginal, may lead to a catastrophic system failure. Such an erroneous control output may be due to either a benign fault or a malicious security attack. As most of the time the plant will be deep within its safe subspace, we can avoid using expensive redundancy techniques and thus, reduce the computational load while still guaranteeing safe operation. When a marginal state of the plant is detected, it will signal the potential presence of a "natural" fault or malicious attack. Our scheme will counter this by switching to a critical mode involving higher levels of redundancy to combat natural failures as well as alternative mechanisms to defeat malicious attacks. A major challenge in our approach is to determine, in real-time, whether the current state of the physical plant is deep within its safe sub-space or is marginal. We have used various machine learning techniques for classifying the state and our results indicate that with a reasonable number of entries in a lookup table and with a short execution time, the required classification can be performed efficiently