# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 5 Conclusions and Discussion  
This research demonstrated the effects of two types of data poisoning attacks, namely label flipping and distance-based attacks, against several machine learning models. The experiment shows that data poisoning attacks can compromise the accuracy and reliability of the machine learning models used in network intrusion detection systems. It was observed that distance-based attacks have the highest probability of causing damage to the models' accuracy. Malicious actors can carry out these attacks to exploit vulnerabilities in the system and cause security breaches, which in turn can pose a serious threat to the security and efficiency of supply chain networks. Therefore, it is essential to develop robust countermeasures to detect and mitigate the effects of these attacks. Further research is needed to develop more effective defences against poisoning attacks and to enhance the security of network intrusion detection systems.  
The effect of label flipping attacks on network anomaly detection models based on neural networks was much investigated in this research, within the scope of a supply chain network. It was shown that these attacks can significantly degrade the performance and robustness of the models, especially when the attack intensity is high, which could lead to the disruption of the supply chain network. However, our study has some limitations that can be addressed in future research, such as that this research has only considered two types of data poisoning attacks, which may not capture the full spectrum of the possible attacks that can be launched against network anomaly detection systems. Other types of data poisoning attacks, such as adding malicious samples, modifying features, or injecting noise, may affect the models differently and require different defences.  
Additionally, our reliance on a single dataset could limit representativeness, requiring future studies to incorporate more diverse datasets reflecting the complexities of supply chain networks. The main finding in this research is the vulnerability of machine learning models in network intrusion detection systems, especially random forest, to data poisoning attacks, which significantly reduce their precision and reliability. Conversely, the logistic regression model displayed notable resilience. This research significantly contributes to understanding the intricacies of data poisoning attacks, paving the way for more robust intrusion detection systems. The findings of this research also inform senior managers of the risks associated with the integrity of technical controls in the changing cyber security landscape and of how to ensure business continuity using robust measures to mitigate such vulnerabilities.  
Methodology, U.J.B., O.H. and K.H.; validation, K.S., B.H. and H.a.-K.; investigation, all authors; formal analysis O.H. and K.H.; writing--original draft, U.J.B., O.H. and K.H.; writing--review and editing, K.S., B.H. and H.a.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data available in a publicly accessible repository that does not issue DOIsPublicly available datasets were analyzed in this study. This data can be found here: ([[https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)]([https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html](https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html)), accessed on 15 November 2023).  
The authors declare no conflict of interest.