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".

翻译：实时系统通常实现为响应刺激并在已知有界时间内完成任务的反应式系统。此类系统的开发过程通常涉及使用周期精确的仿真环境，乃至能够精确模拟系统及其运行环境的数字孪生系统。此外，许多实时系统要求高可靠性并力求免疫于安全攻击。因此，开发环境必须支持与可靠性相关的事件，如传感器故障、子系统失效以及可预见的网络安全攻击事件。本文提出SCART框架——一种创新解决方案，旨在扩展实时系统仿真环境的能力，使其能够纳入可靠性相关事件与高级网络安全攻击，例如针对单一传感器的攻击，以及旨在改变传感器组行为的"复杂安全攻击"。我们通过将新提出的环境应用于无人机飞行控制系统（包括其使用机器学习算法的导航系统）的控制验证了系统的有效性。此类系统极具挑战性，因为需要大量难以通过实际系统实现的实验。研究表明，使用SCART效率极高，可提升模型精度并显著降低误报率。其中部分实验还通过一组"真实无人机"进行了验证。