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

The Wireless Sensor Networks (WSNs) are undergoing rapid implementation in various fields like environmental monitoring, health care, and military surveillance. But the open and distributed nature of WSNs exposes them seriously to attacks like unauthorized access of the data, node capturing, and data injection attacks. In this research, I am presenting the enhancement of a further Advanced Intrusion Detection System for Wireless Sensor Networks with the application of machine learning techniques to strengthen the security. The framework employs the XGBoost and CatBoost classifiers for effective attack detection based on diverse features of the network. Furthermore, Explainable AI (XAI) techniques have been integrated into the system to ensure transparency with maximum interpretability, allowing the network administrator to easily comprehend the security decision-making made by the system. The proposed models are evaluated using the WSN-DS dataset from Kaggle, which has many instances labeled for different possible attack scenarios. Compared with conventional Intrusion Detection Systems, our approach showed improvements in performance measures like accuracy, precision, and recall, providing a strong framework to secure WSNs against several attacks. Also, this integration of XAI will provide trust and usability among other stakeholders, thus allowing informed decision-making concerning network defense mechanisms.