Forecasting intrusion in critical power systems infrastructure using Advanced Autoregressive Moving Average (AARMA) based intrusion detection for efficacious alert system

Cyber intrusions into critical infrastructure inflict economic and physical damage. Extensive research is needed to identify and mitigate intrusions in power grid infrastructure. The modern solution is to use a data science time-series approach to identify the intrusion based on the electric grid data collected from the sensors. This paper addresses the new vision of the data science time-series modelling approach to integrate it with the existing power system security system. In this paper, the Advanced Autoregressive Moving Average (AARMA) model is designed to detect the possible intrusion of the given data set. An attack forecast is a model to predict possible cyber intrusions using real-time data input from sensors. By investigating the statistical properties of the sensors’ data set, intrusion detection is possible with a high accuracy of about 90%. Using AARMA, the operators have the benefit of an effective alert system to adjust their configuration and other resource allocation to tackle intrusions with low impact. MATLAB software is used to monitor the IEEE 9-bus and IEEE 33-bus test system against possible cyber-attacks using the proposed AARMA model.