Abstract
As cyber-physical systems have become increasingly complex, rising computational demand has led to the ubiquitous use of multicore processors in embedded environments. Size, Weight, Power, and Cost (SWaP-C) constraints have pushed more processes onto shared platforms, including real-time tasks with deadline requirements. To prevent temporal interference among tasks running concurrently or in parallel in such systems, many operating systems provide priority-based scheduling and enforce processor reservations based on Worst-Case Execution Time (WCET) estimates. However, shared resources (both architectural components and data structures within the operating system) provide channels through which these constraints can be broken. Prior work has demonstrated that malicious execution by one or more processes can cause significant delays, leading to potential deadline misses in victim tasks. In this paper, we introduce PolyRhythm, a three-phase attack template that combines primitives across multiple architectural and kernel-based channels, (1) it uses an offline genetic algorithm to tune attack parameters based on the target hardware and OS platform; then (2) it performs an online search for regions of the attack parameter space where contention is most likely; and finally (3) it runs the attack primitives, using online reinforcement learning to adapt to dynamic execution patterns in the victim task. On a representative platform (Raspberry Pi 3B) Poly Rhythm outperforms prior work, achieving significantly more slowdown. As we show for several hardware/software platforms, Poly Rhythm also allows us to characterize the extent to which interference can occur; this helps to inform better estimates of execution times and overheads, towards preventing deadline misses in real-time systems.
