Intrusion detection through SCADA systems using fuzzy logic-based state estimation methods

Supervisory Control And Data Acquisition (SCADA) systems represent a vulnerability in vital infrastructures. For example, an electric power system is subjected to intrusions via its SCADA systems; however, the instrumentation provides detectable variations in response to such interference. Presented herein is a strategy that augments state estimation methods using a Hybrid Fuzzy System for fault monitoring and diagnosis that aims to combine information from multiple domains in order to detect, isolate, identify, and mitigate threats to the system. Furthermore, to endow the state estimation solution methods with some degree of numerical robustness, algorithm-based error detection (ABED) is applied to the Gaussian elimination procedure. Simulation results revealed that ABED provides error detection at low costs and excellent error coverage for floating point arithmetic in the presence of permanent bit and word errors while being free of false alarms and insensitive to both data range and data size.