research-article

Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off

Authors:

Saurav Chakraborty,

Agnieszka Onuchowska,

Brandon WolframAuthors Info & Claims

ACM Transactions on Management Information System (TMIS), Volume 12, Issue 4

Article No.: 37, Pages 1 - 28

https://doi.org/10.1145/3460822

Published: 22 October 2021 Publication History

Abstract

Critical city infrastructures that depend on smart Industrial Internet of Things (IoT) devices have been increasingly becoming a target of cyberterrorist or hacker attacks. Although this has led to multiple studies in the recent past, there exists a paucity of literature concerning real-time Industrial IoT attack detection. The goal of this article is to build a machine-learning approach using Industrial IoT sensor readings for accurately tracking down Industrial IoT attacks in real time. We analyze IoT system behavior under a lab-controlled series of attacks on a Secure Water Treatment (SWaT) system. The system is analytically challenging in that it results in sensor readings that resemble waveforms. To that end, we develop a novel early detection method using functional shape analysis (FSA) to extract features from the data that can capture the profile of the waveform. Our results show an efficiency-complexity trade-off between functional and non-functional methods in predicting IoT attacks.

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Sindiramutty SJhanjhi NAkbar RHussain MRay SAmsaad F(2024)Generative AI for Threat Hunting and Behaviour AnalysisUtilizing Generative AI for Cyber Defense Strategies10.4018/979-8-3693-8944-7.ch007(235-286)Online publication date: 13-Sep-2024
https://doi.org/10.4018/979-8-3693-8944-7.ch007
Kumar SPrasad R(2024)Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)10.1109/ICICNIS64247.2024.10823323(396-402)Online publication date: 17-Dec-2024
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Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 12, Issue 4

December 2021

225 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3483349

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 22 October 2021

Accepted: 01 April 2021

Revised: 01 December 2020

Received: 01 May 2020

Published in TMIS Volume 12, Issue 4

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https://doi.org/10.4018/979-8-3693-8944-7.ch007
Kumar SPrasad R(2024)Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)10.1109/ICICNIS64247.2024.10823323(396-402)Online publication date: 17-Dec-2024
https://doi.org/10.1109/ICICNIS64247.2024.10823323
Manivannan D(2024)Recent endeavors in machine learning-powered intrusion detection systems for the Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2024.103925229(103925)Online publication date: Sep-2024
https://doi.org/10.1016/j.jnca.2024.103925
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