1 Boardwalk Pipelines, Houston, Texas, USA.
2 Jawaharlal Nehru Technological University, Hyderabad, India.
3 Speridian Technologies, Lahore, Pakistan.
4 Lahore Leads University, Lahore, Pakistan.
5 University of Punjab, Lahore, Pakistan
Received on 08 March 2023; revised on 23 June 2023; accepted on 26 June 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.
IoT; Intrusion Detection Systems; AI; Machine Learning; Deep Learning; Resource-Constrained Devices; Cybersecurity; Network Security; Threat Mitigation
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Sridevi Kakolu, Muhammad Ashraf Faheem and Muhammad Aslam. AI-enabled intrusion detection systems in IoT networks: Advancing defense mechanisms for resource-constrained devices. International Journal of Science and Research Archive, 2023, 09(01), 752–769. Article DOI: https://doi.org/10.30574/ijsra.2023.9.1.0316
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0