MAARS: Multi-Rate Attack-Aware Randomized Scheduling for Securing Real-time Systems
<|reference_start|>MAARS: Multi-Rate Attack-Aware Randomized Scheduling for Securing Real-time Systems: Modern Cyber-Physical Systems (CPSs) consist of numerous control units interconnected by communication networks. Each control unit executes multiple safety-critical and non-critical tasks in real-time. Most of the safety-critical tasks are executed with a fixed sampling period to ensure deterministic timing behaviour that helps in its safety and performance analysis. However, adversaries can exploit this deterministic behaviour of safety-critical tasks to launch inference-based-based attacks on them. This paper aims to prevent and minimize the possibility of such timing inference or schedule-based attacks to compromise the control units. This is done by switching between strategically chosen execution rates of the safety-critical control tasks such that their performance remains unhampered. Thereafter, we present a novel schedule vulnerability analysis methodology to switch between valid schedules generated for these multiple periodicities of the control tasks in run time. Utilizing these strategies, we introduce a novel Multi-Rate Attack-Aware Randomized Scheduling (MAARS) framework for preemptive fixed-priority schedulers that minimize the success rate of timing-inference-based attacks on safety-critical real-time systems. To our knowledge, this is the first work to propose a schedule randomization method with attack awareness that preserves both the control and scheduling aspects. The efficacy of the framework in terms of attack prevention is finally evaluated on several automotive benchmarks in a Hardware-in-loop (HiL) environment.<|reference_end|>
@article{sain2024enhancing,
    title={Enhancing Attack Resilience in Real-Time Systems through Variable
  Control Task Sampling Rates},
    author={Arkaprava Sain, Sunandan Adhikary, Ipsita Koley, Soumyajit Dey},
    journal={arXiv preprint arXiv:2408.00341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00341},
    primaryClass={eess.SY cs.CR cs.OS cs.SY}
}