# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 1 Introduction
This multifaceted analysis endeavours to provide insights into these models' predictive capacity and susceptibility to adversarial data manipulation within blockchain-enhanced supply chain networks. Blockchain-enabled supply chain networks represent intricate systems encompassing multiple stakeholders, including suppliers, manufacturers, distributors, retailers, and customers, as shown in Figure 1. These entities engage in diverse processes, such as production, transportation, inventory management, and demand forecasting, all collectively facilitating the efficient and timely delivery of goods and services to customers. However, these supply chain networks face many challenges and risks, including disruptions, uncertainties, cyberattacks, and environmental concerns. One vital challenge pertains to safeguarding the network's security against malicious intruders who may seek to compromise, sabotage, or pilfer sensitive information or resources from within the network. These attacks are especially challenging to detect and prevent in blockchain-enabled supply chain networks, where the network data are distributed and decentralised across multiple nodes [1]. Data poisoning attacks can have serious implications for network security and business profitability, as they can compromise the detection capabilities of NIDSs, allow malicious intrusions to go unnoticed, and disrupt the normal operations of the supply chain network [1]. Therefore, developing effective countermeasures to mitigate the impact of data poisoning attacks on NIDSs in blockchain-enhanced supply chain networks is essential. Several methods can be investigated to defend against these attacks, such as data sanitisation, robust aggregation, and anomaly detection. However, these methods are beyond the scope of this paper and have been left for future work. Future work could also explore other types of data poisoning attacks, such as adding malicious samples, injecting noise, or other methods mentioned in this research, and evaluate their effects on different machine learning models and datasets. Moreover, future work could investigate the impact of data poisoning attacks on the security and efficiency of blockchain-enabled supply chain networks in greater detail and develop more robust and secure network intrusion detection systems for this domain. This research endeavours to investigate the susceptibility of machine learning models, including logistic regression, random forest, SVC, XGB Classifier, and an eight-layer neural network, to data poisoning attacks within network intrusion detection systems (NIDSs) deployed in blockchain-enhanced supply chain networks. By systematically evaluating these models in various attack scenarios and assessing their performance using metrics like F1 Score and accuracy, we aim to provide nuanced insights into the impact of data poisoning on the robustness and security of NIDSs. Our findings contribute to developing more resilient intrusion detection systems tailored to the challenges presented by blockchain-enabled supply chain networks. The supply chain model adopted in this research is a generic network model and applies to all application domains. The paper is organised as follows: Section 2 delves into related work and research implications, encompassing an extensive literature review of the recent and pertinent contributions in the field. Section 3 offers an in-depth exploration of the selected dataset and the rationale behind its selection. Section 4 presents the experimental design, outcomes, and an evaluation of the impact of data poisoning attacks on the machine learning models. Lastly, Section 5 concludes the paper, opening a discussion on potential future endeavours to enhance and expand upon the findings and results obtained in this study.