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Abstract

As computer networks and technologies become increasingly embedded in our daily lives, there is growing concern about detecting intrusions and cyberattacks. It’s still difficult to detect intrusions in computer networks effectively. This is due to the recent trend of cyber attackers posing as intrusion detection systems (IDS) by altering packet contents. Additionally, a large number of new devices are added to computer networks daily. Security concerns in computer networks are also being brought up by these new devices. In the context of the Internet of Things (IoT) and the industrial Internet of things (IIoT), this study aims to present an overview of the body of research on IDS that has used different approaches in anomaly-based, signature-based, network-based, host-based, and hybrid-based IDS. Many papers that examined techniques, results, limitations, and future directions were reviewed. We believe that this academic study offers a roadmap for researchers and industry workers working on IDSs.

Keywords

Intrusion Detection System IDS, deep learning, IoT, IIoT, machine learning, AI

Article Type

Article

First Page

22

Last Page

40

Publication Date

4-30-2025

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