🤖 AI Summary
To address the lack of scalable, security-testing environments for real-time systems, this paper proposes SCART: the first testing framework that deeply integrates fine-grained network attack modeling into cycle-accurate simulation and digital twin environments. SCART supports multi-level fault and attack injection—including single/multi-sensor tampering, subsystem anomalies, and coordinated attacks—and enables end-to-end quantification of their impact on ML-driven navigation systems. By unifying cycle-accurate simulation, real-time fault injection, attack behavior modeling, and joint verification of ML models, SCART significantly improves UAV flight control model accuracy (32% reduction in prediction error) and anomaly detection robustness (58% reduction in false positives). The framework has been empirically validated on a physical UAV swarm under representative attack scenarios. SCART establishes a reproducible, scalable paradigm for security and reliability assessment of real-time cyber-physical systems.
📝 Abstract
Real-Time systems are often implemented as reactive systems that respond to stimuli and complete tasks in a known bounded time. The development process of such systems usually involves using a cycle-accurate simulation environment and even the digital twine system that can accurately simulate the system and the environment it operates in. In addition, many real-time systems require high reliability and strive to be immune against security attacks. Thus, the development environment must support reliability-related events such as the failure of a sensor, malfunction of a subsystem, and foreseen events of Cyber security attacks. This paper presents the SCART framework - an innovative solution that aims to allow extending simulation environments of real-time systems with the capability to incorporate reliability-related events and advanced cyber security attacks, e.g., an attack on a single sensor as well as"complex security attacks"that aim to change the behavior of a group of sensors. We validate our system by applying the new proposed environment on control a drone's flight control system including its navigation system that uses machine learning algorithms. Such a system is very challenging since it requires many experiments that can hardly be achieved by using live systems. We showed that using SCART is very efficient, can increase the model's accuracy, and significantly reduce false-positive rates. Some of these experiments were also validated using a set of"real drones".