Machine Learning Model Data Poisoning Attacks and Countermeasures Research

Currently, machine learning models are widely used in natural language processing, image and video processing, and other fields. However, security issues such as data poisoning attacks threaten their performance and reliability. It is of great significance to study the defense mechanism. Therefore, this paper proposes a defense method based on dynamic network structure adjustment and adaptive learning weights and analyzes its effect in improving the robustness of model poisoning attacks. Experiments show that the accuracy of the model under poisoning attack drops from 100% to 76.67%, and the accuracy increases to 83.33% after adopting the defense mechanism. By dynamically adjusting the network structure and learning weights, the model can adapt to changes in data distribution, reduce the interference of poisoning data, and stabilize the decision boundary without obvious underfitting. This study provides an effective reference for improving the robustness of the machine learning model under data poisoning attacks, which helps to ensure the stable operation of the model in complex environments.