Innovative Approaches to Poisoning Attack Detection with Boosting Algorithms

It is quite difficult to identify and counteract poisoning attempts in ML settings, especially when boosting methods are involved. The goal of this research is to investigate and improve Adaptive Gradient Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting (LightGBM) ability to detect these kinds of attacks. The analysis starts with a thorough preprocessing step to guarantee data integrity and relevance, using the RT-IoT 2022 dataset as a benchmark. After that, several tests are carried out using the previously mentioned boosting methods to build reliable classification models. To maximize performance, each algorithm goes through a rigorous hyperparameter tuning process. In the experimental setting, the dataset is subjected to controlled poisoning attacks, and then boosting techniques are used to identify anomalies. Performance measures including recall, accuracy, precision, and F1-score are used to assess how effective each method is. The outcomes show clear advantages and disadvantages for each algorithm, along with particular situations in which one algorithm may perform better than the others. The trade-offs between computing effectiveness, detection accuracy, and attack vector resistance are highlighted via comparative analysis. In addition, the models’ decision-making procedures and feature importance are revealed, offering a more comprehensive comprehension and interpretability. This work provides a framework for further research in this important area and highlights the potential of boosting algorithms in strengthening the robustness of machine learning systems against poisoning attempts.