On Evading Randomization-Based Defense in Hierarchical Real-Time Systems

Security for real-time systems is increasingly important with the growth of connected real-time systems in safety-critical domains such as automotive, medical, and avionics. A crucial aspect of securing such systems is to understand the attacks that the current techniques cannot effectively safeguard against. Especially relevant are vulnerabilities of real-time systems arising from their rigid temporal guarantees and attacks that exploit such vulnerabilities. Randomization-based defense techniques can reduce side-channel inference, but such techniques are limited due to the strict timing bounds of real-time systems. In this paper, we design and analyze NosyNeighbor , an inter-partition side-channel attack that exploits the timing guarantees of real-time systems to infer the timing parameters of a safety-critical task in a hierarchical system. Using an adaptive technique, NosyNeighbor can improve its inference over time and evade randomization-based defense. Experimental results show that NosyNeighbor can infer victim task execution with a precision of roughly 73% under normal system load, and with a recall of about 35% using multiple malicious tasks across partitions. NosyNeighbor is also effective under the common attack model with two malicious tasks in the system, with a precision of 64%.