Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

作     者：Bar, Shachar Prasad, P.W.C. Sayeed, Md Shohel 

作者机构：School of Computing Mathematics Charles Sturt University Bathurst NSW 2795 Australia International School Duy Tan University Da Nang 550000 Viet Nam Faculty of Information Science and Technology Multimedia University Melaka 75450 Malaysia 

出 版 物：《Computers, Materials and Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第1期

页      面：1-23页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We are grateful to Angelika Maag for proof reading and making corrections to this article. Without her support  it would have not been possible to submit this in the current form 

主　　题：Network intrusion 

摘      要：Escalating cyber security threats and the increased use of Internet of Things (IoT) devices require utilisation of the latest technologies available to supply adequate protection. The aim of Intrusion Detection Systems (IDS) is to prevent malicious attacks that corrupt operations and interrupt data flow, which might have significant impact on critical industries and infrastructure. This research examines existing IDS, based on Artificial Intelligence (AI) for IoT devices, methods, and techniques. The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy, precision, recall and F1-score; this research also considers training time. Results demonstrate that Graph Neural Networks (GNN) have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99% accuracy in a relatively short training time, while also capable of learning from network traffic the inherent characteristics of different cyber-attacks. These findings identify the GNN (a Deep Learning AI method) as the most efficient IDS system. The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection. This research recommends Federated Learning (FL) as the AI training model, which increases data privacy protection and reduces network data flow, resulting in a more secure and efficient IDS solution. © 2024 The Authors. Published by Tech Science Press.