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 ar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/12/549 |
| _version_ | 1797382329238290432 |
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| author | Usman Javed Butt Osama Hussien Krison Hasanaj Khaled Shaalan Bilal Hassan Haider al-Khateeb |
| author_facet | Usman Javed Butt Osama Hussien Krison Hasanaj Khaled Shaalan Bilal Hassan Haider al-Khateeb |
| author_sort | Usman Javed Butt |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:03:51Z |
| format | Article |
| id | doaj.art-b03474174483478f9144fe52512515c8 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-08T21:03:51Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b03474174483478f9144fe52512515c82023-12-22T13:47:01ZengMDPI AGAlgorithms1999-48932023-11-01161254910.3390/a16120549Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain NetworksUsman Javed Butt0Osama Hussien1Krison Hasanaj2Khaled Shaalan3Bilal Hassan4Haider al-Khateeb5Faculty of Engineering and IT, British University in Dubai, Dubai 345015, United Arab EmiratesFaculty of Engineering and Environment, Northumbria University, London NE1 8ST, UKFaculty of Engineering and Environment, Northumbria University, London NE1 8ST, UKFaculty of Engineering and IT, British University in Dubai, Dubai 345015, United Arab EmiratesFaculty of Engineering and Environment, Northumbria University, London NE1 8ST, UKCyber Security Innovation (C.S.I.) Research Centre, Operations & Information Management, Aston University, Birmingham B4 7ET, UKAs 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.https://www.mdpi.com/1999-4893/16/12/549blockchainsupply chainmachine learningflippingpoisoning attacks |
| spellingShingle | Usman Javed Butt Osama Hussien Krison Hasanaj Khaled Shaalan Bilal Hassan Haider al-Khateeb Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks Algorithms blockchain supply chain machine learning flipping poisoning attacks |
| title | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| title_full | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| title_fullStr | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| title_full_unstemmed | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| title_short | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| title_sort | predicting the impact of data poisoning attacks in blockchain enabled supply chain networks |
| topic | blockchain supply chain machine learning flipping poisoning attacks |
| url | https://www.mdpi.com/1999-4893/16/12/549 |
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