# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 2 Related Work
Consequently, RFID and blockchain technology offer the potential to enhance profitability without being significantly impacted by increased discrepancies in the supply chain. Data poisoning attacks can have various goals, such as reducing the overall detection accuracy, causing targeted misclassification or bad behaviour, and inserting backdoors or neural trojans [2;3]. Data poisoning attacks can be classified into two types: indiscriminate and targeted. Indiscriminate attacks aim to degrade the model's overall performance by randomly changing the labels or features of some training examples. Targeted attacks aim to manipulate the model's behaviour on specific inputs or outputs by carefully crafting poisoned examples close to the decision boundary or greatly influencing the model [2;3]. Data poisoning attacks on machine learning models have been studied in various domains, such as computer vision, natural language processing, recommender systems, etc. 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. Previous research by [9] researched poisoning attacks and defences in machine learning by reviewing over 100 articles published in the previous years. By designing a framework that categorises threat models, attacks, and organising existing defences, they also maintained that their systematisation applies to attacks and defences in other data modalitieseven though their primary focus was on computer vision applications. In addition to reviewing the historical development of machine learning models and highlighting present and future obstacles, the study outlined realistic scenarios for launching attacks on them. The research also offered guidance for understanding and defending against these attacks and insights into developing reliable machine-learning models resistant to malicious users. Researchers in [10] used two AML attack types: poisoning and evasion. Poisoning attacks modify the training data to introduce errors or biases in the model. Evasion attacks craft adversarial examples that can fool the model at the time of inference. The article evaluates the impact of these attacks on four popular machine learning models: logistic regression, support vector machine, random forest, and a neural network. It uses two benchmark datasets: NSL-KDD and CICIDS2017. It measures the performance of the models using four metrics: accuracy, precision, recall, and F1 Score. Researchers in [11] evaluated the robustness of federated learning-based network intrusion detection systems under data poisoning attacks by malicious clients. They proposed a new attack method called PT-GAN and a new defence method based on poisoned sample detection. Several mitigations and defences against data poisoning attacks were proposed in [12]; the researchers proposed a defence mechanism independent of the specific type of poisoning attack, called De-Pois. It leverages a mimic model that approximates the behaviour of the target model when trained on clean samples. It employs Generative Adversarial Networks (GANs) to augment the training data and train the mimic model. It detects poisoned samples by measuring the prediction discrepancy between the mimic and target models. Other researchers [6] propose a model-level defensive mechanism based on poisoned model detection and a data-level defensive mechanism based on poisoned data detection. The proposed model-level defence boosts its detection accuracy by up to 48% under the poisoning attacks on UNSW-NB15 dataset and 36% on CICIDS2018 dataset, and the proposed data-level defence further improves its detection accuracy by up to 13% on CICIDS2018 dataset. Another study [13] discusses a novel approach to protect against data poisoning attacks that can be used in NIDSs. The discussed approach, DPA-FL, is based on federated learning, allowing multiple participants to collaboratively train a global model without sharing their local data.