Network-based intrusion detection: a comparative analysis of machine learning approaches for improved security

This research paper reports a detailed evaluation and improvement of the machine learning techniques for network-based intrusion detection systems (IDS). We start by proposing a new Network-based Intrusion Detection Machine Learning (NIDML) model, a learning model that is an ensemble of Decision Trees, Random Forests, K-nearest neighbors, Neural Networks, and Ensemble methods. Each of the algorithms were trained and tested on the LUFlow dataset, which varies from 93.03% to 99.96% accuracy of the obtained models. This paper aims at making a comparison between NIDML model and recent high-performing IDS techniques as well as pointing out the merits and the demerits. We discuss how such issues as accuracy, adaptability, and scalability are enhanced through the use of machine learning in IDS performance. While NIDML model shows promising results, we acknowledge limitations in generalizability and adaptability to unseen attacks. The last section of the study provides recommendations for future research focus areas; this includes testing against emerging threats and various possible situations in the real world to make further improvements to more efficient intrusion detection systems.