Identification of Data Injection Attacks in Networked Control Systems with Varying Setpoint Condition

The Industry 4.0, which widely uses Networked Control Systems (NCS), plays a relevant role in nowadays society and can be threatened by cyberattacks. In this context, recent literature presents a countermeasure that uses white gaussian noise and a Noise Impulse Integration technique to identify controlled data injection attacks in NCSs. Despite the high accuracy of such countermeasure when the NCS setpoint does not vary, the literature indicates that it presents loss of performance when the NCS setpoint is slowly changing. To overcome this constraint, this work proposes a novel approach to improve the performance of the referred countermeasure in order to make it resilient to variations in the NCS setpoint.