Ensemble Learning Based Intrusion Detection for Wireless Sensor Network Environment

doi:10.23940/ijpe.24.09.p2.541551

Abstract

Abstract: WSNs are integral to various applications, ranging from environmental monitoring to industrial automation. However, their vulnerability to malicious activities necessitates robust security measures. The proposed Ensemble Intrusion Detection System (ENS-IDS) leverages machine learning techniques to detect anomalies in the WSN data, identifying potential intrusions or security breaches. The system incorporates feature selection, model training, and real-time monitoring to enhance its accuracy and responsiveness. Evaluation metrics, including precision, recall, and F1 score, demonstrate the effectiveness of the ENS-IDS in mitigating security threats within the WSN environment. The presented ENS-IDS is evaluated on KDD and CICIDS2017 dataset and comparison on known classifiers such as SVM, random forest, extra tree, KNN, logistic regression, decision tree and ensemble classifiers such as XGBoost, CatBoost and LGBM. Our model ENS-IDS has given better accuracy, precision, recall and F1-score.

Key words: intrusion detection, ensemble learning, boosting, machine learning, wireless sensor network

Vikas Kumar, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Ensemble Learning Based Intrusion Detection for Wireless Sensor Network Environment [J]. Int J Performability Eng, 2024, 20(9): 541-551.

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