Cyber-physical systems (CPSs) in modern real-time applications integrate numerous control units linked through communication networks, each responsible for executing a mix of real-time safety-critical and non-critical tasks. To ensure predictable timing behaviour, most safety-critical tasks are scheduled with fixed sampling periods, which supports rigorous safety and performance analyses. However, this deterministic execution can be exploited by attackers to launch inference-based attacks on safety-critical tasks. This paper addresses the challenge of preventing such timing inference or schedule-based attacks by dynamically adjusting the execution rates of safety-critical tasks while maintaining their performance. We propose a novel schedule vulnerability analysis methodology, enabling runtime switching between valid schedules for various control task sampling rates. Leveraging this approach, we present the Multi-Rate Attack-Aware Randomized Scheduling (MAARS) framework for preemptive fixed-priority schedulers, designed to reduce the success rate of timing inference attacks on real-time systems. To our knowledge, this is the first method that combines attack-aware schedule randomization with preserved control and scheduling integrity. The framework's efficacy in attack prevention is evaluated on automotive benchmarks using a Hardware-in-the-Loop (HiL) setup.

翻译：现代实时应用中的信息物理系统（CPS）集成了大量通过通信网络连接的控制器单元，每个单元负责执行实时安全关键任务与非关键任务的混合任务集。为确保可预测的时序行为，多数安全关键任务采用固定采样周期进行调度，这为严格的安全性与性能分析提供了支撑。然而，这种确定性执行模式可能被攻击者利用，对安全关键任务发起基于推理的攻击。本文通过动态调整安全关键任务的执行速率同时维持其性能，以应对此类时序推理或基于调度攻击的防御挑战。我们提出了一种新颖的调度脆弱性分析方法，能够在不同控制任务采样率对应的有效调度方案之间实现运行时切换。基于此方法，我们提出了面向抢占式固定优先级调度器的多速率攻击感知随机调度（MAARS）框架，旨在降低实时系统中时序推理攻击的成功率。据我们所知，这是首个将攻击感知的调度随机化与控制及调度完整性保持相结合的方法。通过硬件在环（HiL）实验平台，我们在汽车基准测试中评估了该框架在攻击防御方面的有效性。