Machine Learning Based Intrusion Detection Systems Using HGWCSO And ETSVM Techniques

Abstract

In recent years, computer networks have grown significantly in size and complexity, and Intrusion Detection Systems (IDS) have become an integral part of the system foundation. An IDS must overcome obstacles such as a low detection rate and a high computational load. Insufficient feature selection in IDS can have a negative impact on the accuracy of machine learning methods, resulting in errors in the form of False Negatives (FN) and False Positives (FP), which must be minimised. The research presents an effective feature selection and classification technique for intrusion detection by combining the Hybrid Grey Wolf optimizer Cuckoo Search Optimization (HGWCSO) with the Enhanced Transductive Support Vector Machine (ETSVM). The proposed strategies are capable of selecting the top eight features from a total of 41 features without sacrificing precision or recall. The experimental results reveal that the proposed system outperforms the current system in terms of accuracy, precision, recall, and F-measure.