Published Paper Details:

The significance of intrusion detection systems (IDS) in network security and the need for accurate and effective detection methods are emphasised by research on the subject. Research emphasises the use of statistical methods in host-based systems, web-based data mining systems, and modern intrusion detection systems such as firewalls. It offers a thorough synopsis and evaluation of the body of knowledge about this topic. Using web-based data mining and machine learning algorithms--such as Rough Set Theory and Support Vector Machine--as well as an optimised framework and a two-layer mechanism, the article investigates intrusion detection systems. Effective feature selection and representation are the foundation of machine learning-based intrusion detection systems (IDS). For feature extraction, anomaly detection, abuse detection, and hybrid models, new approaches are required. It's also necessary to have domain adaptability, interpretable models, visualisation, and strong defensive mechanisms. Compared to conventional techniques, the suggested learning-based intrusion detection system may identify network traffic patterns more precisely, minimizing false positives and logging network activity. Although it can be flexible and scalable to many kinds of attacks and network circumstances, it could have issues with interpretability, resource needs, and possible hostile manipulation vulnerabilities.