# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### Experiment Analysis
The results show that all models have a lower accuracy and F1 Score on this dataset than their original values, indicating that this attack successfully degrades their performance. The logistic regression, Figure 8a, model is still the most resilient to this attack, as it has the smallest decrease in performance (0.868 to 0.88 for its F1 Score). Table 4 shows the performance metrics of all models on the poisoned dataset generated using the distance-based label flipping attack. The results show that this attack has a lower success rate than the random label flipping attack, as it affects fewer instances in the training data. The logistic regression model in distance-based label flipping attack, Figure 9a is again the most resilient to this attack, as it has the highest accuracy and F1 Score on this dataset (0.549 and 0.706, respectively). Figures 10 and 11 show the loss and accuracy curves of the ANN model on the non-poisoned and poisoned datasets, respectively. The figures show that the ANN model has high accuracy on both datasets but lower precision and recall than other models. The results indicate that data poisoning attacks are a serious threat to machine learning models based on IDPSs and the supply chain networks that depend on them. To defend against data poisoning attacks, several methods have been proposed in the literature, such as data sanitisation, robust aggregation, and anomaly detection. Future work can explore these methods and evaluate their effectiveness in preventing or mitigating data poisoning attacks on supply chain networks. The KDDCUP'99 dataset offers valuable insights into network intrusion detection, but it does have limitations, including outdated data and imbalanced classes. These limitations may render intrusion detection models less effective. Therefore, data poisoning attacks could exacerbate these issues by further compromising the quality and reliability of models, thus increasing the risk of false positives and false negatives in identifying network intrusions. It can also be noted that some features in the dataset are highly correlated with the attack labels, meaning that they are more likely to indicate an attack when they have higher values. These features are crucial for the effectiveness of intrusion detection models. However, if attackers manipulate the training data through data poisoning, these correlations may be disrupted, causing the models to become less reliable. Furthermore, as discussed in the third section, the distribution of attack and normal traffic in the network anomaly dataset emphasises that normal traffic is more consistent and stable, making it easier to detect anomalies. However, data poisoning attacks can skew this balance by injecting misleading data, potentially making it more challenging to differentiate between normal and attack traffic. In addition, the effects of data poisoning attacks on supply chain networks can be related to the potential disruption of the overall network's flow of goods and services. Data poisoning attacks can exacerbate these challenges by introducing misleading data, thus impeding the normal flow of network traffic and potentially causing disruptions, bottlenecks, and delays. These attacks pose a significant threat to the machine learning models used in network intrusion detection systems and the supply chain networks that rely on these models. It is important to underscore the importance of defending against data poisoning attacks and the need for robust security measures to protect both network integrity and the broader supply chain network. These security measures should also consider the dataset limitations and analysis highlighted in our third section to ensure that intrusion detection systems remain effective in an ever-evolving threat landscape. It was also demonstrated that data poisoning attacks can seriously jeopardise the security and effectiveness of supply chain networks by drastically reducing the precision and dependability of machine learning models built for network intrusion detection systems.