A Machine Learning Based Cyber Attack Detection Model for Wireless Sensor Networks in Microgrids

The expanding dependence on remote sensor systems (WSNs) inside microgrids has underscored the basic require for strong security components, given their helplessness to different cyber-attacks . This study introduces a machine learning-based cyberattack detection model tailored for WSNs in microgrids, utilizing a comprehensive dataset from Kaggle. Our demonstrate coordinating a assorted set of calculations, counting Convolutional Neural Systems (CNN), Detached Forceful Classifiers, Arbitrary Timberland Classifiers, and XGBoost Classifiers, to guarantee tall exactness and productivity in recognizing peculiarities. By nourishing the framework with organize information, it can precisely classify the organize state as either typical or beneath one of three particular assault sorts: grayhole, blackhole, or flooding assault. This multifaceted approach not as it were upgrades the discovery .