• C. Murugesh,

Wireless Sensor Networks (WSNs) play a vital role in various applications, including environmental monitoring, industrial automation, and surveillance. However, the open and distributed nature of WSNs exposes them to security threats, making intrusion detection a critical concern. Traditional rule-based intrusion detection systems often struggle to cope with the evolving and complex nature of attacks. To address this challenge, this study presents a novel approach that introduces a Whale Optimization Algorithm with Machine Learning Driven Intrusion Detection in Wireless Sensor Networks (WOAML-IDWSN) technique. The presented WOAML-IDWSN technique integrates the inclusion of WOA with ML approach for effective intrusion detection in WSNs. It comprises two major processes such as WOA based feature selection and ML based intrusion detection. At the initial stage, the WOA is applied to electing an optimal subset of features. Next, in the second stage, extreme gradient boosting (XGBoost) classifier is applied for the identification of the intrusions. Extensive experiments are conducted using benchmark datasets to evaluate the effectiveness of the WOAML-IDWSN approach. Extensive comparative analyses against existing intrusion detection techniques demonstrate the superiority of the WOAML-IDWSN technique in accurately identifying various intrusion types.

"Whale Optimization Algorithm with Machine Learning Driven Intrusion Detection in Wireless Sensor Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.a339-a353, September-2023, Available :http://www.jetir.org/papers/JETIR2309042.pdf

"Whale Optimization Algorithm with Machine Learning Driven Intrusion Detection in Wireless Sensor Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppa339-a353, September-2023, Available at : http://www.jetir.org/papers/JETIR2309042.pdf