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

Arkaprava Sain, Sunandan Adhikary, Ipsita Koley, Soumyajit Dey

TL;DR

This paper proposes a novel schedule vulnerability analysis methodology, enabling runtime switching between valid schedules for various control task sampling rates, and presents 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.

Abstract

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.

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

TL;DR

This paper proposes a novel schedule vulnerability analysis methodology, enabling runtime switching between valid schedules for various control task sampling rates, and presents 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.

Abstract

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.
Paper Structure (17 sections, 6 equations, 10 figures, 4 tables, 1 algorithm)

This paper contains 17 sections, 6 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Task and Scheduler Model
  4. Control Task Model
  5. Closed Loop Under Sampling Period Variation
  6. Residue-based Anomaly Detector
  7. Threat Model
  8. Attacker's Capabilities
  9. Attack Execution
  10. Motivating Example
  11. Proposed Methodology
  12. Performance-aware multi-rate Period Selection
  13. Secure Sampling Period Selection and Schedule Generation
  14. Schedule Vulnerability Analysis
  15. Schedule Selection Algorithm
  16. ...and 2 more sections

Figures (10)

  • Figure 1: System and Control Task Model
  • Figure 2: Attack Effective Window
  • Figure 3: Ladder diagram of Task-Set-1 illustrated along with their corresponding AEI and AAI.
  • Figure 4: AEW-based scheduling of Taskset-2
  • Figure 5: Feasible Schedules Generated using Task-Set 2 (Considering $p_1=2$)
  • ...and 5 more figures

Theorems & Definitions (7)

  • Claim 1
  • Definition 1
  • Claim 2
  • proof
  • Remark 1
  • Definition 2
  • Definition 3