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...
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| Other Authors: | , , , , |
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2023
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2989 https://doi.org/10.3390/a16120549. |
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| _version_ | 1862980610608332800 |
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| author | Javed Butt , Usman |
| author2 | Hussien, Osama Hasanaj, Krison Shaalan, Khaled Hassan, Bilal al-Khateeb, Haider |
| author2_role | author author author author author |
| author_facet | Javed Butt , Usman Hussien, Osama Hasanaj, Krison Shaalan, Khaled Hassan, Bilal al-Khateeb, Haider |
| author_role | author |
| dc.creator.none.fl_str_mv | Javed Butt , Usman Hussien, Osama Hasanaj, Krison Shaalan, Khaled Hassan, Bilal al-Khateeb, Haider |
| dc.date.none.fl_str_mv | 2023 2025-05-13T13:41:29Z 2025-05-13T13:41:29Z |
| dc.identifier.none.fl_str_mv | Usman Javed Butt et al. (2023) “Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks,” Algorithms, 16(12), p. 549. 1999-4893 https://bspace.buid.ac.ae/handle/1234/2989 https://doi.org/10.3390/a16120549. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | Algorithmsv16 n12 (2023): 549 |
| dc.subject.none.fl_str_mv | blockchain; supply chain; machine learning; flipping; poisoning attacks |
| dc.title.none.fl_str_mv | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks |
| dc.type.none.fl_str_mv | Article |
| 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. |
| id | budr_d4f8233229bfa00e5af2b5debf41a360 |
| identifier_str_mv | Usman Javed Butt et al. (2023) “Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks,” Algorithms, 16(12), p. 549. 1999-4893 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2989 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain NetworksJaved Butt , UsmanHussien, OsamaHasanaj, KrisonShaalan, KhaledHassan, Bilalal-Khateeb, Haiderblockchain; supply chain; machine learning; flipping; poisoning attacksAs 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.MDPI2025-05-13T13:41:29Z2025-05-13T13:41:29Z2023ArticleUsman Javed Butt et al. (2023) “Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks,” Algorithms, 16(12), p. 549.1999-4893https://bspace.buid.ac.ae/handle/1234/2989https://doi.org/10.3390/a16120549.enAlgorithmsv16 n12 (2023): 549oai:bspace.buid.ac.ae:1234/29892025-05-13T13:44:34Z |
| spellingShingle | Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks Javed Butt , Usman 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://bspace.buid.ac.ae/handle/1234/2989 https://doi.org/10.3390/a16120549. |