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Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain

Moradpoor, Naghmeh; Barati, Masoud; Robles-Durazno, Andres; Abah, Ezra; McWhinnie, James


Masoud Barati

Andres Robles-Durazno

Ezra Abah

James McWhinnie


The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues have been poorly addressed which has resulted in a growing number of cyberattacks with increasing impact and huge consequences. There are many machine learning solutions to detect anomalies against this type of infrastructure given the popularity of such an approach in terms of accuracy and success in detecting zero-day attacks. However, machine learning algorithms are prone to adversarial attacks. In this paper, an energy-consumption-based machine learning approach is proposed to detect anomalies in a water treatment system and evaluate its robustness against adversarial attacks using a novel dataset. The evaluations include three popular machine learning algorithms and four categories of adversarial attack set to poison both training and testing data. The captured results show that although some machine learning algorithms are more robust against adversarial confrontations than others, overall, the proposed anomaly detection mechanism which is built on energy consumption metrics and its associated dataset are vulnerable to such attacks. To this end, a blockchain approach to protect the data during the training and testing phases of such machine learning models is proposed. The proposed smart contract is deployed in a public blockchain test network and their costs and mining time are investigated.

Presentation Conference Type Conference Paper (Published)
Conference Name Cyber Science 2022: International Conference on Cybersecurity, Situational Awareness and Social Media
Start Date Jun 20, 2022
End Date Jun 21, 2022
Acceptance Date May 2, 2022
Online Publication Date Mar 8, 2023
Publication Date 2023
Deposit Date May 5, 2022
Publicly Available Date Mar 9, 2024
Publisher Springer
Pages 437-451
Book Title Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media
ISBN 978-981-19-6413-8
Keywords Adversarial attacks, Machine learning, Critical national infrastructure, Industrial control systems, Water treatment systems, Blockchain
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