Vulnerability Analysis and Mitigation of Directed Timing Inference Based Attacks on Time-Triggered Systems

Much effort has been put into improving the predictability of real-time systems, especially in safety-critical environments, which provides designers with a rich set of methods and tools to attest safety in situations with no or a limited number of accidental faults. However, with increasing connectivity of real-time systems and a wide availability of increasingly sophisticated exploits, security and, in particular, the consequences of predictability on security become concerns of equal importance. Time-triggered scheduling with offline constructed tables provides determinism and simplifies timing inference, however, at the same time, time-triggered scheduling creates vulnerabilities by allowing attackers to target their attacks to specific, deterministically scheduled and possibly safety-critical tasks. In this paper, we analyze the severity of these vulnerabilities by assuming successful compromise of a subset of the tasks running in a real-time system and by investigating the attack potential that attackers gain from them. Moreover, we discuss two ways to mitigate direct attacks: slot-level online randomization of schedules, and offline schedule-diversification. We evaluate these mitigation strategies with a real-world case study to show their practicability for mitigating not only accidentally malicious behavior, but also malicious behavior triggered by attackers on purpose.