Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

Abstract

Objective. Wireless sensor networks, crucial for various applications, face growing security challenges due to the escalating complexity and diversity of attack behaviours. This paper presents an advanced intrusion detection algorithm, leveraging feature-weighted Naive Bayes (NB), to enhance network attack detection accuracy. Methodology. Initially, a feature weighting algorithm is introduced to assign context-based weights to different feature terms. Subsequently, the NB algorithm is enhanced by incorporating Jensen–Shannon (JS) divergence, feature weighting, and inverse category frequency (ICF). Eventually, the improved NB algorithm is integrated into the intrusion detection model, and network event classification results are derived through a series of data processing steps applied to corresponding network traffic data. Results. The effectiveness of the proposed intrusion detection algorithm is evaluated through a comprehensive comparative analysis using the NSL-KDD dataset. Results demonstrate a significant enhancement in the detection accuracy of various attack types, including normal, denial of service (DoS), probe, remote-to-local (R2L), and user-to-root (U2R). Moreover, the proposed algorithm exhibits a lower false alarm rate compared to other algorithms. Conclusion. This paper introduces a wireless network intrusion algorithm that not only ensures improved detection accuracy and rate but also reduces the incidence of false detections. Addressing the evolving threat landscape faced by wireless sensor networks, this contribution represents a valuable advancement in intrusion detection technology.