An effective intrusion detection system in the smart grid using whale optimization algorithm and supported vector machine

Smart power grids require physical systems for power generation, transmission, and distribution, in addition to information systems for energy management and monitoring data collection. The operating conditions of the equipment used for power transmission and conversion have a significant impact on the safe and stable operation of smart grids because they constitute an important component of the urban power grid. This study presents a new intrusion detection algorithm that can classify cyber attacks on smart power systems. Whale Optimization Algorithms (WOA) and Support Vector Machine (SVM) form the foundation of the intrusion detection paradigm. To reduce the detection error, the support vector machine parameters are adjusted using the whale technique. The problems of attacks, both failure detection and prediction in a power system can be solved with the proposed SVM-WOA model. In order to demonstrate the effectiveness of the proposed model and provide empirical findings, we used the NSL-KDD database. Based on the findings, the proposed technique has a detection accuracy of ۹۸.۷% for cyber attacks on smart power systems.

Keywords:

intrusion detection , whale optimization algorithm , supported vector machine