# Enhanced Blockchain-Based Data Poisoning Defense Mechanism [PERSON]
## 1 Introduction  
The stability of modern artificial intelligence (AI) models is essential for various applications and devices [1, 2, 3, 4]. Machine learning (ML) techniques are increasingly integrating additional technologies to tackle cybersecurity challenges. As new vulnerabilities arise, these threats can compromise the integrity of the ML models, which attackers specifically target [5]. Notably, data poisoning (DP) attacks are designed to undermine the integrity of a model during both the training and testing stages. Hence, there is a pressing need to develop advanced defense strategies that bolster the resilience of these models against potential DP threats while ensuring the overall reliability of AI-based systems [6]. AI-adapted decision systems have seen extensive development capitalizing on the availability of data to enhance their efficiency [7]. These traffic datasets often serve as comprehensive records that encompass various attributes, features, and relevant information. The enormous volume of network traffic carrying such attributes within centralized systems makes it challenging to maintain the effectiveness of AI-driven cybersecurity models [8, 9, 10, 11]. Security threats posed to machine learning frameworks are generally categorized into two phases: training and testing (or inference). Importantly, data poisoning (DP) attacks can manifest during both phases, presenting significant risks to the integrity of these models (see Figure 1). DP not only encompasses the inclusion of corrupted data samples but also involves data that lead to unauthorized alterations of hyper-parameters in AI models through illicit means [6]. A prevalent form of DP attack includes the injection of malicious samples into the targeted training set, which disrupts either the feature values or the labels of the trainingexamples during the training phase, thereby affecting the model reliability by modifying its hyper-parameters [6]. DP attacks can take place not only during the training phase but also specifically during the testing phase, where the emphasis is often on altering model parameters as opposed to corrupting the test samples themselves [12; 13]. In this context, many studies have found that the predominant defense strategies implemented during testing prioritize the protection of the integrity of testing nodes. Rather than directly filtering or securing the data, these strategies aim to safeguard the framework within which testing occurs [6; 14]. In this context, an advanced method for range ambiguity suppression in spaceborne SAR systems utilizing blind source separation has been proposed, enhancing robust signal processing techniques for DP detection [15]. Recent developments have also introduced Trusted Execution Environments (TEEs), which serve as secure execution environments and provide substantial protection for both the code and data, thereby strengthening overall cybersecurity measures. These TEEs ensure that the information contained within them remains confidential and maintain its integrity against potential threats [14]. While TEEs have been extensively studied within specific artificial intelligence (AI) systems, the methodologies for developing these secure environments are applicable beyond just AI scenarios [6]. Additionally, various non-AI-specific defense mechanisms can contribute to protecting trained machine learning models during the testing phase [16; 17]. Furthermore, innovative blockchain-based solutions have emerged as effective methods for ensuring integrity across interconnected nodes, making these techniques adaptable for use within AI systems to counteract DP attacks. The implementation of blockchain-based defense mechanisms has gained attention due to their potential to ensure the integrity of connected nodes throughout distributed networks. These mechanisms have been meticulously designed to not only enhance the security of the network as a whole but also manage the risks associated with data exposure and manipulation.