A Feasibility Area Approach for Early Stage Detection of Stealthy Infiltrated Cyberattacks in Power Systems

Advanced stealthy cyberattacks are capable of infiltrating the cybersecurity layers of power grids and alter their operating conditions, resulting in adverse effects on the system performance. Detecting such Stealthy Infiltrated Cyberattacks (SICA) at the earliest opportunity becomes crucial in order to enable power system operators to implement appropriate corrective measures. To that end, this paper proposes the addition of a new cybersecurity layer for SICA after they have broken through existing cyberattack prevention layers. The paper develops the Feasibility Area (FA) as a classifier mechanism to detect SICA in the collected data of Power System State Variables (PSSV). The proposed detection layer consists of two computational stages. The first stage involves estimating the FA parameters through a historical window of data over a specified period of time, which is then inputted to the second stage. In the second stage, the position of each PSSV with respect to the estimated FA is assessed and utilized by the SICA detection mechanism to identify broken through attacks. A flag vector is created indicating the location of each PSSV with respect to the defined FA. The location of each PSSV and its pattern represented in the flag vector are utilized to identify the existence of SICA. Various SICA detection mechanisms using mathematical techniques and the Pattern Recognition Neural Network (PRNN) have been applied. The numerical results from the evaluation of the proposed FA approach demonstrate a promising performance in detecting the SICA using the proposed method.