Classification of intrusion cyber-attacks in smart power grids using deep ensemble learning with metaheuristic-based optimization

The most advanced power grid design, known as a 'smart power grid', integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber-physical architecture enables bidirectional, real-time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure (AMI). Data protection issues, such as data tampering, firmware exploitation, and the leakage of sensitive information arise due to the smart power grid's substantial reliance on ICT. To maintain reliable and efficient power distribution, these issues must be identified and resolved quickly. Intrusion detection is essential for providing secure services and alerting system administrators in the case of adversary attacks. This paper proposes an intrusion classification scheme that identifies several types of cyber attacks on modern smart power grids. Grey-Wolf metaheuristic optimization-based feature selection is used to learn non-linear, overlapping, and complex electrical grid properties. An extended deep-stacked ensemble technique is advanced by putting predictions from weak learners (CNNs) into a meta-learner (MLP). The outcomes of this approach are explained and confirmed using explainable AI (XAI). The publicly available dataset from Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) is used to conduct experiments. The experimental results show that the proposed method achieved a peak accuracy of 96.6% while scrutinizing the original MSU-ORNL data feature set and a maximum accuracy of 99% when analysing the selected feature set. Therefore, the proposed intrusion classification scheme may protect smart power grid systems against cyber security attacks.