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arXiv 2408.00341eess.SYcs.CRcs.OScs.SY
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通过可变控制任务采样率增强实时系统的攻击弹性

Enhancing Attack Resilience in Real-Time Systems through Variable Control Task Sampling Rates

Arkaprava Sain, Sunandan Adhikary, Ipsita Koley, Soumyajit Dey

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12 pages including references, Total 10 figures (with 3 having subfigures)
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AI总结：

本文提出MAARS框架，通过动态调整安全关键任务执行速率和运行时切换调度，结合攻击感知随机化与控制完整性，有效防御实时系统中的时序推理攻击。

摘要：

现代实时应用中的信息物理系统（CPSs）集成了大量通过通信网络连接的控制单元，每个单元负责执行实时安全关键和非关键任务的混合。为确保可预测的时序行为，大多数安全关键任务以固定采样周期调度，这支持了严格的安全性和性能分析。然而，这种确定性执行可能被攻击者利用，对安全关键任务发起基于推理的攻击。本文通过在保持性能的同时动态调整安全关键任务的执行速率，解决了防止此类时序推理或基于调度攻击的挑战。我们提出了一种新颖的调度漏洞分析方法，允许在各种控制任务采样率的有效调度之间进行运行时切换。利用该方法，我们提出了用于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，旨在降低实时系统上时序推理攻击的成功率。据我们所知，这是首个将攻击感知调度随机化与保持控制及调度完整性相结合的方法。该框架在攻击预防方面的有效性通过使用硬件在环（HiL）设置的汽车基准进行了评估。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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