[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120831-en":3,"doc-seo-120831-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120831,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks","As computer networks become increasingly important across domains, secure and reliable operation is critical for blockchain-enabled supply chain networks. Intrusion detection systems (IDSs) detect anomalies and attacks, yet they can be degraded by data poisoning attacks, including label and distance-based flipping. This study experimentally evaluates the impact of such attacks on network intrusion detection using multiple machine learning models—logistic regression, random forest, SVC, and XGB classifier—measuring performance with F1 score, confusion matrix, and accuracy. Each model is tested under clean data, 20% random label flipping, and distance-based label flipping (threshold 0.5), alongside an eight-layer neural network using accuracy metrics and classification reports. The results provide guidance for building more robust IDSs for blockchain supply chain environments.","algorithms  \nArticle  \nPredicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks  \nUsman Javed Butt 1, Osama Hussien 2, Krison Hasanaj 2, Khaled Shaalan 1, Bilal Hassan 2 and Haider al-Khateeb 3, *  \nCitation: Butt, U.J.; Hussien, O.; Hasanaj, K.; Shaalan, K.; Hassan, B.; al-Khateeb, H. Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks. Algorithms 2023, 16, 549 . [https://doi.org/10.3390/a16120549](https://doi.org/10.3390/a16120549)  \nAcademic Editor: Yue Duan  \nReceived: 14 October 2023  \nRevised: 15 November 2023  \nAccepted: 22 November 2023  \nPublished: 29 November 2023  \nCopyright: © 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Engineering and IT, British University in Dubai, Dubai 345015, United Arab Emirates; [usman.butt@buid.ac.ae](usman.butt@buid.ac.ae) (U.J.B.); [khaled.shaalan@buid.ac.ae](khaled.shaalan@buid.ac.ae) (K.S.)  \n2 Faculty of Engineering and Environment, Northumbria University, London NE1 8ST, UK; [ossama.akram@northumbria.ac.uk](ossama.akram@northumbria.ac.uk) (O.H.); [krison.hasanaj@northumbria.ac.uk](krison.hasanaj@northumbria.ac.uk) (K.H.); [b.hassan@northumbria.ac.uk](b.hassan@northumbria.ac.uk) (B.H.)  \n3 Cyber Security Innovation (C.S.I.) Research Centre, Operations & Information Management, Aston University, Birmingham B4 7ET, UK  \n* [Correspondence: h.al-khateeb@aston.ac.uk](Correspondence: h.al-khateeb@aston.ac.uk)  \nAbstract: 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 XGBClassifier, 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.  \nKeywords: blockchain; supply chain; machine learning; flipping; poisoning attacks  \n1. Introduction  \nIn recent years, network intrusion detection systems (NIDSs) have become essential tools for securing computer networks, especially in blockchain-enabled supply chain networks. These systems often rely on machine learning models to enhance their effectiveness. However, these models are vulnerable to data poisoning attacks, compromising their accuracy and posing significant security risks. This research aims to conduct an experimental assessment of the impact of data poisoning attacks on the machine learning models used within NIDSs.  \nThe ever-expanding realm of computer net","cbCaimvrtCgrY6nU","https://ap.wps.com/l/cbCaimvrtCgrY6nU","pdf",4360755,1,22,"English","en",105,"# Introduction\n## Background: NIDS and blockchain-enabled supply chains\n## Data poisoning attacks and their forms\n## Research goals and contributions","[{\"question\":\"What problem does the paper address in blockchain-enabled supply chain networks?\",\"answer\":\"It examines how data poisoning attacks can affect the effectiveness of network intrusion detection systems that use machine learning within blockchain-enabled supply chain environments.\"},{\"question\":\"Which types of data poisoning attacks are evaluated?\",\"answer\":\"The study evaluates label flipping with random flipping (20%) and distance-based label flipping with a distance threshold of 0.5.\"},{\"question\":\"How are the machine learning models evaluated for intrusion detection performance?\",\"answer\":\"Models are evaluated using F1 score, confusion matrix analysis, and accuracy, and the neural network is assessed with accuracy metrics and classification reports.\"}]","Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks | PDF",1785732254,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-impact-of-data-poisoning-attacks-in-blockchain-enabled-supply-chain-networks","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-impact-of-data-poisoning-attacks-in-blockchain-enabled-supply-chain-networks/120831/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in blockchain-enabled supply chain networks?","Question",{"text":75,"@type":76},"It examines how data poisoning attacks can affect the effectiveness of network intrusion detection systems that use machine learning within blockchain-enabled supply chain environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of data poisoning attacks are evaluated?",{"text":80,"@type":76},"The study evaluates label flipping with random flipping (20%) and distance-based label flipping with a distance threshold of 0.5.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models evaluated for intrusion detection performance?",{"text":84,"@type":76},"Models are evaluated using F1 score, confusion matrix analysis, and accuracy, and the neural network is assessed with accuracy metrics and classification reports.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]