Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks


As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Cyber Security Innovation (CSI) Research Centre
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).
Uncontrolled Keywords: blockchain,flipping,machine learning,poisoning attacks,supply chain,Computational Mathematics,Theoretical Computer Science,Numerical Analysis,Computational Theory and Mathematics
Publication ISSN: 1999-4893
Last Modified: 12 Apr 2024 07:20
Date Deposited: 04 Dec 2023 12:17
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Related URLs: https://www.mdp ... -4893/16/12/549 (Publisher URL)
https://kdd.ics ... 9/kddcup99.html (Related URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-11-29
Accepted Date: 2023-11-22
Authors: Butt, Usman javed
Hussien, Osama
Hasanaj, Krison
Shaalan, Khaled
Hassan, Bilal
Al-Khateeb, Haider (ORCID Profile 0000-0001-8944-123X)



Version: Published Version

License: Creative Commons Attribution

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