# Enhanced Blockchain-Based Data Poisoning Defense Mechanism [PERSON]
## 1 Introduction
By distributing information across a wide array of nodes and employing cryptographic techniques, these networks enhance their defenses and create a more secure operational environment compared to traditional setups. By distributing data across numerous nodes and utilizing cryptographic technologies, blockchain networks are better equipped to guard against attacks that could have severe repercussions in centralized environments, thereby enhancing their overall security posture in the digital landscape [16; 17]. The main contributions of this paper are significant advancements in the integration of blockchain technology into a robust data poisoning (DP) defense system specifically designed for distributed collective AI model networks. Key achievements include the development of the Blockchain Governance Game (BGG) by incorporating artificial intelligence (AI) methodologies, which enhances its effectiveness in cybersecurity applications. Another crucial aspect is the framework developed to effectively prevent 51 percent attacks, thereby safeguarding the integrity of the network against majority control threats. Additionally, the BGG controller uniquely operates at the intersection of blockchain and AI, utilizing machine learning (ML) and deep learning (DL) algorithms for superior security performance. The innovative BGG controller comprises two key components: the Predictor, which uses a convolutional neural network (CNN) to foresee potential attacks by predicting when an adversary may acquire over half of the nodes; and the BGG decision engine, designed based on BGG theory. This newly proposed controller is designed to deliver high cybersecurity performance even with limited data collection and minimal computational power, ensuring resource efficiency while maintaining robust protection measures. The proposed method features an innovative application of the Blockchain Governance Game (BGG) framework to develop advanced data poisoning (DP) defense strategies, setting it apart from standard techniques. Unlike typical approaches that mainly concentrate on filtering damaged data or securing systems, this method takes a proactive stance by anticipating potential DP attack scenarios. It utilizes a Predictor component that employs convolutional neural networks (CNNs) to enhance its predictive capabilities and fortify defenses effectively. This proactive strategy facilitates prompt defensive maneuvers, thereby safeguarding the integrity of artificial intelligence (AI) systems throughout their operational stages. Moreover, the incorporation of blockchain technology fortifies data integrity and security via decentralized validation and immutable ledger frameworks, effectively mitigating risks that centralized systems might inadvertently overlook. The collaboration of machine learning algorithms within the BGG elevates adaptive responses and resource optimization, distinctly highlighting the originality and innovative essence of this data poisoning (DP) defense mechanism in contrast to traditional methods within the cybersecurity landscape. This paper is organized as follows: The basic knowledge of a DP attack and its defense mechanism has been explained in this section. The theoretical background of the BGG and the BGG-based DP defense network structure are introduced in Section 2. In Section 3, the BGG controller, which is the core component of the BGG-based DP defense network, is introduced. The BGG controller in the control center is the decision system of the machine-learning-enabled Blockchain Governance Game, and its sub-components, the Predictor and the BGG engine, are also explained in this section. The simulation result of the BGG controller is provided in Section 4. Lastly, the conclusion is shown in Section 5.