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".