# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 1 Introduction  
In 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. The ever-expanding realm of computer networks, coupled with the proliferation of applications across them, underscores the burgeoning significance of network security. Intrusion detection systems (IDSs) have evolved into specialised instruments proficient in meticulously identifying network anomalies and potential attacks, a trend that has recently garnered prominence. The intrusion detection domain has traditionally centred on two fundamental techniques: anomaly-based and misuse-based. While misuse-based detection ispreferred in commercial applications due to its predictability and heightened accuracy, academic research frequently champions anomaly-based detection for its theoretical potential in countering novel and unprecedented attacks. Data poisoning attacks represent a form of adversarial assault on machine learning models, where malicious actors manipulate training data to compromise the model's accuracy or induce misclassifications. In the specific context of network intrusion detection systems within blockchain-enabled supply chain networks, poisoning attacks assume a critical role by potentially enabling attackers to circumvent the system's detection mechanisms. These poisoning attacks come in various forms, including label flipping and distance-based flipping attacks, all of which have demonstrated their efficacy in undermining the integrity of machine learning models. Our research's main goals, objectives, and contributions are as follows:  
* A range of machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, were systematically evaluated for network intrusion detection in blockchain-enhanced supply chain networks
* Rigorous evaluation of each model incorporated metrics such as their F1 Score, confusion matrix analysis, and accuracy assessments. * The robustness of each model against data poisoning attacks was tested by subjecting them to various scenarios, such as no attack, random label flipping with 20% randomness, and distance-based label flipping with a 0.5 distance threshold. * An eight-layer neural network was also experimented with, and its performance was assessed using accuracy and a classification report library. * Comprehensive insights were provided into the effects of data poisoning attacks on machine learning models and their implications for network security. * This research contributed to developing more robust and secure network intrusion detection systems for blockchain-enabled supply chain networks. The emphasis on the intersection of data poisoning attacks and supply chain networks underscores the importance of securing these pivotal components within contemporary information systems. In the context of blockchain-enabled supply chain networks, various machine learning models, including logistic regression, random forest, SVC, and XGBClassifier, have been harnessed to identify network anomalies and potential attacks. The assessment of these models has encompassed performance metrics such as F1 Score, confusion matrix, and accuracy. Additionally, a neural network model was meticulously trained using the KDDCUP99 dataset, followed by a comprehensive evaluation using accuracy and F1 Score metrics. To gauge the resilience of these models in the face of data poisoning attacks, they were subjected to two specific scenarios: random label flipping with a randomness factor of 20% and distance-based flipping attacks characterised by a distance threshold set at 0.5.