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:实时系统通常被实现为响应刺激并在已知的有限时间内完成任务的反应式系统。这样的系统开发过程通常涉及使用循环精度仿真环境,甚至使用数字线系统,可以准确模拟系统和其所运行的环境。此外,许多实时系统需要高可靠性,并力求免疫安全攻击。因此,开发环境必须支持与可靠性相关的事件,例如传感器故障、子系统故障和预见到的网络安全攻击事件。本文介绍了SCART框架——一种创新的解决方案,旨在允许扩展实时系统的模拟环境,使其具备纳入可靠性相关事件和高级网络安全攻击(例如单个传感器的攻击以及旨在改变一组传感器行为的“复杂安全攻击”)的能力。我们通过将新提出的环境应用于控制一架无人机的飞行控制系统及其使用机器学习算法的导航系统来验证了我们的系统。这种系统非常具有挑战性,因为它需要许多实验,这在使用实际系统几乎不可能实现。我们证明了使用SCART非常高效,可以提高模型的准确性,并显着降低误报率。这些实验中的一些也使用了一组“真实的无人机”进行验证。