Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks

Abstract

Wireless sensor networks (WSNs) are an emerging military and civilian technology that uses sensors. Sensor networks are hierarchical and chaotic in remote, unmonitored sites. Wireless sensor networks pose unique security threats due to their public location and wireless transmission. WSNs are vulnerable to various routing attacks, including Black holes, Sybil, sinkholes and wormholes. In this paper, we proposed advanced intrusion detection systems based on hybrid machine learning (AIDS-HML) in wireless sensor networks to identify and classify attacks. Hybrid machine learning classifiers identify wireless sensor network dangers. Benchmark datasets are used to compare the proposed model to baseline models in terms of precision, recall, f1-score, and accuracy. The scheme is trained and evaluates prediction models. This confirms that the detection accuracy achieved 99.80% using the NSL-KDD benchmark dataset based on hybrid random forest and extreme gradient boost (RF-XGB). The hybrid cluster labelling K-Means (CLK-M) s achieved better classification accuracy of 100% using UNSW_NB15, and CICIDS2017 benchmark datasets for binary classification of label attacks. Different attack detection metrics were compared against various benchmark datasets to evaluate the quality of this work. The proposed system is efficient in simulations for feature extraction and route discovery and detection attacks achieving an accuracy of 99.46%.