AI-enabled intrusion detection systems in IoT networks: Advancing defense mechanisms for resource-constrained devices
Sridevi Kakolu, Muhammad Ashraf Faheem, Muhammad Aslam · International Journal of Science and Research Archive · 2023
With the rapid expansion of Internet of Things (IoT) networks comes an urgent need for advanced security mechanisms to combat ever more sophisticated cyber threats. Although intrusion detection systems (IDS) are vital, more is needed to detect IoT intrusion in resource-limited IoT devices with constrained processing capabilities and memory. In this paper, we discuss how Artificial Intelligence (AI) can be embedded into IDS frameworks to improve the security of IoT networks. Using available resources, machine learning, and deep learning techniques in AI-enabled IDS can enhance detection and mitigate intrusions. The existing methodologies are reviewed comprehensively, implementation challenges are assessed, and potential future research directions are discussed in this study. The conclusions show that AI-based IDS can significantly enable a more secure and resilient IoT ecosystem, provide innovative defense strategies, and open up opportunities for next-generation network security solutions.