Intrusion detection using search-based learning optimized ensemble tree classifier model

An Intrusion Detection System (IDS) is an important component of cybersecurity, meant to monitor malicious behaviour, detect, and respond to unauthorized activities in computer systems or networks. Generally, Intrusion detection (IDS) is classified into host-based IDS (HIDS) and network-based IDS (NIDS), which monitor individual devices and network traffic, respectively. Existing models faced certain limitations, including the dilemma of balancing false positives against false negatives, the challenge of adjusting to evolving threats, handling issues with high-dimensional information and encrypted traffic, and limited resource competence when dealing with privacy concerns. The proposed research work currently aims at developing an intrusion detection system that is more adaptive and effective to hinder these existing challenges and improve the security of digital environments. The study is related to applying an elaborate Search-based learning-optimized ensemble tree classifier (SBO-based ensemble tree classifier) for improving ID in Vehicular Ad Hoc Networks (VANETs). The ensemble classifier incorporates decision tree, random forest, extra tree, and eXtreme Gradient Boosting (XG Boost) classifiers, which are fused to provide a comprehensive interpretation of potential attacks within the VANET environment. Moreover, the research is enriched by incorporating Search-based learning optimization that takes advantage of their collective and adaptive nature. This innovative amalgamation attempts to perfect the aggregated response generated by the ensemble classifier, which fine-tunes the proposed model for effective intrusion detection. To facilitate the multi-dimensional orientation, four separate outputs, such as alpha, beta, gamma, and delta, were introduced, which allow the categorization of intrusion attacks based on specific types. More specifically, the experimental results illustrate that the proposed SBO-based ensemble tree classifier achieved superior performance with an accuracy of 96.56%, F1-score of 96.63%, FPR of 0.97, MCC of 0.97, Precision of 96.59%, Sensitivity of 96.68%, and Specificity of 96.52% for intrusion detection and outperforms the other existing methods using the BOT-IOT Dataset.