Detecting Spoofed Messages Using Clock Skew
Two researchers are working on a system to detect spoofed messages sent to automobiles by fingerprinting the clock skew of the various computer components within the car, and then detecting when those skews are off. It's a clever system, with applications outside of automobiles (and isn't new).
To perform that fingerprinting, they use a weird characteristic of all computers: tiny timing errors known as "clock skew." Taking advantage of the fact that those errors are different in every computer -- including every computer inside a car -- the researchers were able to assign a fingerprint to each ECU based on its specific clock skew. The CIDS' device then uses those fingerprints to differentiate between the ECUs, and to spot when one ECU impersonates another, like when a hacker corrupts the vehicle's radio system to spoof messages that are meant to come from a brake pedal or steering system.
Paper: "Fingerprinting Electronic Control Units for Vehicle Intrusion Detection," by Kyong-Tak Cho and Kang G. Shin.
Abstract: As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thus-derived fingerprints are then used for constructing a baseline of ECUs' clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors -- a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055%. Unlike state-of-the-art IDSs, if an attack is detected, CIDS's fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.
Posted on July 20, 2016 at 7:26 AM • 33 Comments