A Deep and Systematic Review of the Intrusion Detection Systems Based on Machine Learning and Deep Learning Techniques
Nikhita Guhan, Sindhu Ravindran, M Ramachandran, Vikneswaran Vijean · 2024
Intrusion detection systems are the primitive elements in safeguarding vital infrastructures, which are able to identify malicious activity on hosts or networks. The efficient IDS must be able to handle a variety of threats is a difficult security cum threat related problems. Numerous strategies have been put out in the literature as of right now to increase intrusion detection efficiency while taking into account some of these restrictions, like mobility and resource limitations. This review paper discusses about the early research works on performed on IDS using Deep Learning and Machine Learning techniques. This paper provides a qualitative assessment of these methods and deeper insights about the analysis as well as a summary of the field's current themes and the methods used to train and implement IDSs. We address the overall limitations of these solutions, namely the types of attacks that these approaches are unable to identify and the setup limitations that are specific to each of these works, even though they offer insightful information and solutions for certain aspects of these constraints.