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2024 | OriginalPaper | Chapter

An AI-Powered Network Intrusion Detection System in Industrial IoT Devices via Deep Learning

Authors : Mohammad Shahin, F. Frank Chen, Ali Hosseinzadeh, Enrique Contreras Lopez, Hamed Bouzary, Hamid Khodadadi Koodiani

Published in: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems

Publisher: Springer Nature Switzerland

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Abstract

The chapter delves into the critical need for robust network intrusion detection systems in Industrial Internet of Things (IIoT) devices, driven by the increasing vulnerability to cyber-attacks. It introduces various Machine Learning (ML) and Deep Learning (DL) algorithms, highlighting their application in detecting IoT botnet attacks. The study utilizes the N-BaIoT dataset, which encompasses network traffic data from commercial IoT devices infected by notorious botnets like Mirai and Gafgyt. The chapter compares the performance of different DL models, such as Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Attention-Based Long Short-Term Memory (ALSTM), among others. The results demonstrate varying accuracies across different devices, with some models, like the 2-CNN, achieving exceptional performance. The chapter concludes by emphasizing the importance of AI-driven systems in enhancing the security of IIoT devices, offering valuable insights into the most effective algorithms for botnet attack detection.

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Title: An AI-Powered Network Intrusion Detection System in Industrial IoT Devices via Deep Learning
Authors: Mohammad Shahin
F. Frank Chen
Ali Hosseinzadeh
Enrique Contreras Lopez
Hamed Bouzary
Hamid Khodadadi Koodiani
Publisher: Springer Nature Switzerland
Book: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems
Print ISBN: 978-3-031-38164-5

Electronic ISBN: 978-3-031-38165-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-38165-2_131