Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach

被引:2
作者
Birahim, Shaikh Afnan [1 ]
Paul, Avijit [2 ]
Rahman, Fahmida [3 ]
Islam, Yamina [3 ]
Roy, Tonmoy [4 ]
Hasan, Mohammad Asif [2 ]
Haque, Fariha [2 ]
Chowdhury, Muhammad E. H. [5 ]
机构
[1] Univ Glasgow, Sch Comp Sci & Engn, Glasgow G12 8QQ, Scotland
[2] Rajshahi Univ Engn & Technol, Dept Elect & Telecommun Engn, Rajshahi 6204, Bangladesh
[3] Int Islamic Univ Chittagong, Dept Comp Sci & Engn, Chittagong 4318, Bangladesh
[4] Utah State Univ, Dept Data Analyt & Informat Syst, Logan, UT 84322 USA
[5] Qatar Univ, Dept Elect Engn, Doha, Qatar
关键词
Intrusion detection system; wireless sensor networks; particle swarm optimization; ensemble machine learning; explainable AI; streamlit web application; SMOTE;
D O I
10.1109/ACCESS.2025.3528341
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wireless Sensor Networks (WSN) play a pivotal role in various domains, including monitoring, security, and data transmission. However, their susceptibility to intrusions poses a significant challenge. This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. The system addresses key challenges such as the imbalanced nature of datasets and the evolving complexity of network attacks. By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. Extensive experimentation on WSN-DS dataset demonstrates the system's efficacy, achieving an accuracy of 99.73%, with precision, recall, and F1 score values of 99.72% each, outperforming existing approaches. This work offers a robust, scalable solution for securing WSNs, contributing to both academic research and practical applications.
引用
收藏
页码:13711 / 13730
页数:20
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