# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### Experiment Analysis
The random forest model proved to be the most susceptible to both kinds of attacks, whereas the logistic regression model had the highest level of resilience. Additionally, as it affected fewer occurrences in the training data, we discovered that the distance-based label flipping approach had a lower success rate than the random label flipping attack. This research contributes to the body of knowledge on supply chain networks and network intrusion detection systems by examining the impact of data poisoning attacks on machine learning models in this field. Additionally, the design and development of more resilient and secure network intrusion detection systems that can handle the difficulties and dangers of data poisoning assaults would benefit from our research. However, our study has a few drawbacks that may be resolved in future studies. A limitation of this study is the examination of only two categories of data poisoning attacks, which may not encompass the entire range of potential attacks that could be directed towards network intrusion detection systems. Different attacks that cause data poisoning, such as introducing malicious samples, changing features, or adding noise, could affect the models differently and demand distinct countermeasures. Another drawback is that the tests we conducted only employed one dataset, which might not accurately represent the complexity and diversity of network traffic and attacks in the real world. More realistic and varied datasets that can represent the features and dynamics of supply chain networks afforded by blockchain technology could be used in future studies. Further studies may also examine improved efficient techniques for identifying and minimising data poisoning assaults on network intrusion detection systems. Data sanitisation, robust aggregation, and anomaly detection are some of these potential techniques. Before feeding the training data to the model, data sanitisation tries to eliminate or fix any malicious or noisy samples. The goal of robust aggregation is to decrease the influence of poisoned models or poisoned learners by combining the outputs of several models or learners. Finding instances where data or a model deviate from expected or normal behaviour is the goal of this anomaly detection technique. Other data poisoning attacks such as gradient-based poisoning attacks, which use the gradient information of the target model to generate poisoned samples that can alter the model's parameters or decision boundary [31; 32; 33], or meta-learning poisoning attacks, which use meta-learning techniques to generate poisoned samples, could also be investigated in future research. These attacks can be applied to models such as linear models, neural networks, or kernel methods. To summarise our findings, the following can be said about our research results. * The logistic regression model is the most resilient to both attacks because it is a linear model with a simple and stable decision boundary less affected by the label changes. The label changes only affect the instances close to the decision boundary, which are fewer in number and have less influence on the model's parameters. The logistic regression model also has a regularisation term that prevents overfitting and reduces its sensitivity to noise or outliers. * The random forest model is the most vulnerable to both types of attacks because it is an ensemble model that combines the outputs of multiple decision trees trained on random data subsets. The label changes affect most instances in each subset, leading to high variance and inconsistency among the decision trees. The random forest model also has high complexity and flexibility which make it prone to overfitting and increase its susceptibility to noise or outliers. * The support vector machine model is moderately resilient to both attacks because it is a kernel-based model that uses a nonlinear transformation to map the data to a higher dimensional space where it can find a linear decision boundary that separates the classes.