# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 2 Related Work  
Data poisoning attacks on machine learning models have been studied in various domains, such as computer vision, natural language processing, and recommender systems [2; 3]. For instance, the paper by [4] proposes a low-cost data poisoning attack algorithm named AttackRegion-UCB (AR-UCB) within the Attack against Federated Learning-based Autonomous Vehicle (ATT_FLAV) framework. This label flipping attack algorithm offers a unique dynamic black-box target attack for nonlinear regression models updated through federated learning. AR-UCB is designed to maximise attack rewards in rounds of continuous updates, with limited data and model output information available in each round. The research validates the effectiveness of the proposed attack, which demonstrates sequential improvements in attack rewards and can defend against classical aggregation schemes. Figure 1: The supply chain network. Additionally, an innovative framework for conducting online data poisoning attacks against smart-grid cyber-physical systems, based on the online regression task model, was introduced by [5]. The primary goal of this framework is to manipulate the model by gradually contaminating the incoming data stream. Furthermore, it offers a method for choosing data points depending on the sample loss within this framework. Experiments were carried out on an edge device utilising a simulated data stream created from offline open datasets relevant to the smart grid to evaluate the effectiveness of the proposed algorithms. The experimental findings show that this approach can increase the average attack efficacy by more than 1.23 times while reducing time overhead by more than 50%. These results highlight how crucial it is to protect against poisoning attacks in the context of smart grid security. The paper focused on popular online prediction models appropriate for edge intelligence applications and real-time, resource-constrained settings. However, the application of machine learning in cyber security, especially in network intrusion detection systems (NIDSs), poses unique challenges and opportunities for both attackers and defenders [6]. NIDSs monitor network traffic and detect malicious activities, such as denial-of-service attacks, port scanning, malware infection, etc. NIDSs can be classified as signature-based or anomaly-based [7]. Signature-based NIDSs rely on predefined rules or patterns to identify known attacks, while anomaly-based NIDSs use machine learning models to learn the normal behaviour of network traffic and flag any deviations as anomalies [6]. Machine learning models can improve the accuracy and efficiency of NIDSs by automatically adapting to changing network conditions and discovering new attack patterns. However, machine learning models are also vulnerable to data poisoning attacks, which involve tampering with the training data to degrade the performance or compromise the integrity of the models [2;6]. The blockchain operates more seamlessly, providing increased security for all participants through transparency. As a new block is added, each node has a copy of the updated chain, which could improve transparency in many fields. The study in [8] presents a three-layer supply chain model that incorporates both RFID and blockchain technologies. The model focuses on the reverse flow of materials and demonstrates that employing blockchain-based RFID technology was profitable for the system in all scenarios. Discrepancies and holding costs particularly influenced the extent of profit increase, as these two parameters were most affected by the combination of the RFID and blockchain. The study highlights the benefits of adopting RFID and blockchain technology in supply chain operations. However, they emphasise the importance of addressing high misplacement rates within the system. It is shown that using RFID and blockchain technology can lead to a significant profit increase of up to 61% in the supply chain, even in high discrepancies.