Article
Smart Intrusion Detection in Wireless Sensor Networks Using Optimized Ensemble Learning
The use of WSNs is gaining significance in the real-time data gathering, environmental monitoring, and security. But it is easy to be hacked into them and undermine their security and reliability. An explainable ensemblebased intrusion detection system was developed using the Kaggle Wireless Sensor Network Dataset that was capable of addressing this issue. In the case of hyperparameter optimization, the algorithm is Particle Swarm Optimization (PSO) and GridSearchCV. It also applies various models of machine learning, including DT + PSO, RF + PSO,KNN + PSO, XGB + PSO, and a hybrid ensemble based on LightGBM and ExtraTree, (RF + DT) + PSO, (RF + KNN) + PSO, (RF + KNN + XGB) + The model results were elucidated with the help of AI methods that could be described, such as LIME and SHAP, which simplified the intrusion detection process. The experiment indicated that the stacking ensemble performed better than any single model and accurately detected threats with 98.1 precision and recall and F1-score. It demonstrates that it is a good and comprehensible solution to WSN intrusion detection. The web application is a Flaskbased application that allows users to log in and add features dynamically, prepare attacks and categorize them into the list of Normals, Blackhole, Flooding, Grayhole, and TDMA.
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