Data-Driven Intrusion Detection Techniques for Secure Wireless Sensor Networks Using Machine Learning
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P130Keywords:
Wireless Sensor Networks, Intrusion Detection System, Denial of Service (DoS) Attacks, Machine Learning, SHAP Analysis, LEACH ProtocolAbstract
Denial of Service (DoS) attacks are a major concern for wireless sensor networks (WSNs), which are widely used in IoT-based real-time monitoring applications. In this paper, an intrusion detection framework based on data is proposed to secure communication in WSN using machine learning techniques. The WSN-DS dataset is used with LEACH protocol for Flooding, TDMA Scheduling, Blackhole and Grayhole attack detection. Prior to training the model, the dataset is subjected to data preparation methods such as encoding, normalization, balancing with SMOTE, and train–test splitting. Following their implementation, the Decision Tree and K-Nearest Neighbors (KNN) classifiers are assessed using several metrics such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), ROC-AUC, confusion matrix, and SHAP analysis. It is clear from the experimental results that the KNN model (with an ACC rate of 99.39% and an F1-score of 99.38%) and the Decision Tree model (with a ROC-AUC of 99.43%) perform well in the intrusion detection task. The proposed models are compared with other existing methods such as CatBoost, Naïve Bayes, SVC, RF and D-GOPA, which have proved that the proposed models outperform the other methods in terms of detection ACC and classification reliability. The proposed framework considerably increases the efficiency of the security that can be provided for the network, the efficiency with which attacks can be detected and it offers an efficient solution for secure, intelligent applications of wireless sensor networks.
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