Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
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.
Description
Keywords
blockchain; supply chain; machine learning; flipping; poisoning attacks
Citation
Usman Javed Butt et al. (2023) “Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks,” Algorithms, 16(12), p. 549.