Intrusion Detection System Using Artificial Intelligence

Abstract

Numerous software and hardware platforms are used by the tens of billions of computers connected by the Internet to provide commercial and communication services. The networked nature of computers, however, allows for resource misuse and online attack by malevolent parties. The development of flexible, adaptive security-oriented methods is extremely difficult, given the steadily expanding number of Internet threats. One of the most crucial tools for spotting Internet threats is the intrusion detection system (IDS). To create effective an IDS, several strategies from diverse fields have been used in literature. AI-based techniques have various advantages over other techniques and play a significant role in the development of IDS. However, there hasn't been a thorough analysis of AI-based intrusion detection algorithms to look at and comprehend where they stand right now. The development of IDS has been the main emphasis of this chapter's review of several AI-based approaches. The source of the audit data, the processing standards, the procedure, the dataset, the classifier design, in related investigations, the experimental environment design, and feature reduction method utilized have all been compared. AI-based approaches' advantages and drawbacks have been highlighted. The chapter will help to clarify the numerous directions that IDS work has gone in. For anyone interested in using AI-based methods for IDS and associated issues, the conclusions of this chapter's analysis of the literature are helpful and advantageous. Future directions for this field of study are also provided in the chapter.