PolyRhythm: Adaptive Tuning of a Multi-Channel Attack Template for Timing Interference

Ao Li, M. Sudvarg, Han Liu, Zhiyuan Yu, Chris Gill, Ning Zhang, H. Liu, yu. zhiyuan
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引用次数: 4

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.
PolyRhythm:多通道定时干扰攻击模板的自适应调谐
随着信息物理系统变得越来越复杂,不断增长的计算需求导致多核处理器在嵌入式环境中的普遍使用。尺寸、重量、功率和成本(SWaP-C)的限制将更多的进程推向了共享平台,包括具有截止日期要求的实时任务。为了防止在这些系统中并发或并行运行的任务之间的时间干扰,许多操作系统提供基于优先级的调度,并根据最坏情况执行时间(WCET)估计强制执行处理器预留。然而,共享资源(操作系统中的体系结构组件和数据结构)提供了可以打破这些约束的通道。先前的工作已经证明,一个或多个进程的恶意执行可能会导致严重的延迟,导致受害任务可能错过截止日期。本文介绍了一种基于多架构和基于内核通道的三阶段攻击模板PolyRhythm:(1)基于目标硬件和操作系统平台,使用离线遗传算法来调整攻击参数;然后(2)在线搜索攻击参数空间中最有可能发生争用的区域;最后(3)运行攻击原语,使用在线强化学习来适应受害者任务中的动态执行模式。在代表性平台(Raspberry Pi 3B)上,Poly Rhythm的性能优于先前的工作,实现了更大的减速。正如我们对几个硬件/软件平台所示,Poly Rhythm还允许我们描述干扰可能发生的程度;这有助于更好地估计执行时间和开销,防止在实时系统中错过截止日期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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