Resilient Scheduling of Real-Time Cyber-Physical Systems Against Memory-Corruptions

Real-time cyber-physical systems (CPS) are increasingly deployed in command and control applications for safetyand mission-critical domains such as autonomous vehicles and critical infrastructure. To enable enhanced capabilities, CPS are becoming more complex and interconnected, yet this expanded functionality introduces new security vulnerabilities. Addressing these challenges, this paper presents a secure and resilient scheduling technique for hard real-time CPS applications that protects against common memory-corruption-based attacks. Our approach introduces a security-oriented dimension of criticality, enabling the system to selectively drop low-security-critical workloads in response to detected threats. This reduces the attack surface and allows for the timely rescheduling of both victim task re-executions and system recovery processes. We demonstrate that traditional mixed-criticality scheduling approaches are overly conservative and inadequate for accommodating dynamic recovery requirements under this security model. To address this, we propose a novel scheduling algorithm tailored for securityaware CPS, along with a schedulability test using a securitycriticality demand-bound function. The proposed framework is implemented in FreeRTOS with micro-ROS and validated using a hardware-in-the-loop simulation of a flight control task. Extensive schedulability experiments reveal that our model outperforms existing approaches with required adaptation, improving acceptance ratios by over 30 percent in heavily utilized CPS environments. This work advances secure, real-time scheduling to enhance both the resilience and safety of critical cyber-physical applications.