Intrusion detection system and mitigation of threats in IoT networks using AI techniques: A review - Francis E, Geo; S. Sheeja (Engineering and Applied Science Research, 2023)
In recent times, IoT has been used in a wide range of applications for improving the quality of life. Conversely, IoT turns out to be progressively a superlative target for malicious attacks due to its huge range of openness, distributed nature, and objects. However, for maintaining IoT system security, there is a need for an effective Intrusion detection system (IDS), standing as a fundamental tool in the cyber security environment, which implements a detector that uninterruptedly observes the network traffic. Therefore, the network requires an efficient IDS system for detecting various attacks. Various IDS systems have been implemented for detecting intrusion in the IoT network; however, it is required to have a review of recent developments. The present study, therefore, reviews a range of existing IDS models that are employed in IoT networks for detecting intrusion along with recent threats. Various datasets employed in IDS and the challenges faced by IDS are also explored in this study. This study is implemented with a futuristic vision to improve the existing IDSs competent enough to face the latest attacks and threats in IoT Networks.