# Enhanced Blockchain-Based Data Poisoning Defense Mechanism [PERSON]
## 3 Novel Design of BGG Controller for DP Defense
### The Predictor  
The Predictor in the BGG controller provides the best moment of decision-making \(\tau_{v-1}\) based on the inputs, which are mainly the numbers of genuine and corrupted nodes (\(A_{k}\), \(H_{k}\), and \(A_{0}\)) from (4)-(7). The BGG engine provides the status of the network at the time \(\tau_{v}\). The hyper-parameters \(M\) (total number of nodes in the network) and \(\delta\) from (8) are pre-configured. Other parameters \(\lambda_{A}\) and \(\lambda_{H}\) (the average numbers of corrupted and genuine nodes in the input data) are statistically obtained from the input data during the training phase. It is noted that the overall performance of the combined system will be measured instead of measuring the performance of the Predictor individually (see Section 4). A convolutional neural network (CNN) has been selected for the design of the Predictor in the BGG controller because CNNs and their variants for cybersecurity have been highly successful in classification tasks across different research domains [27]. The CNN for the Predictor within the BGG controller has been selected because of their established effectiveness in classification tasks, particularly within the field of cybersecurity. CNNs are highly skilled at identifying complex patterns and structures in data, making them particularly suited for analyzing network node behavior during potential DP attacks. In contrast to other predictive models that may face challenges with high dimensionality or non-linear relationships, CNNs can effectively handle large datasets while preserving accuracy. The predictive power of CNNs, showcased by achieving an impressive accuracy rate of 96% in training simulations, highlights their dependability in anticipating attack patterns and enabling timely defensive actions. The enhanced performance driven by the complexity of the CNN significantly strengthens decision-making processes crucial for safeguarding AI models against the vulnerabilities posed by DP attacks. Therefore, integrating CNN technology into the BGG framework emerges as a prudent and strategic decision. A CNN with 10 hidden layers has been adapted to calculate the moment of releasing reserved ledgers (i.e., \(\tau_{v-1}\)) in the defense center and predict the result of attacks where an attacker takes a blockchain network by governing more than a half of total nodes. The Predictor predicts the moment of executing the safety mode \(\tau_{v-1}\) as the output of the system. The output indicates the moment of a preliminary action, which depends on the behaviors of both an attacker and a defender. These behaviors are the inputs of the machine learning component. The output of the Predictor are the moments of executing the safety mode \(\tau_{v-1}\), which mainly depend on the inputs of the system. The ML technique generates a multi-variable regression function as a machine learning engine to predict the optimal moment of an action \(\tau_{v-1}\), and the 3D illustration of a regression is shown in Figure 5 for demonstration purposes. The figure provides the relationship between the number of honest nodes and attack nodes with the moment of execution \(\tau_{v-1}\). The x-axis illustrates the number of honest nodes, while the y-axis indicates the number of attack nodes, both ranging from 0 to 50. The z-axis measures \(\tau_{v-1}\), which can reach up to 15 s. The gradient  
Figure 4: BGG Controller. of color from blue to green suggests changes in the computational time required under varying conditions of honest and attack nodes. This visualization conveys how the balance between honest and attack nodes affects the performance of the system and response time in a cybersecurity context. It is noted that a different dataset might draw a different shape of graph because it generates a different regression function.