  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".
