Detecting and Mitigating Data Poisoning Attacks in Machine Learning: A Weighted Average Approach

Abstract

Adversarial attacks, in particular data poisoning, can affect the behavior of machine learning models by inserting deliberately designed data into the training set. This study proposes an approach for identifying data poisoning attacks on machine learning models, the Weighted Average Analysis (VWA) algorithm. This algorithm evaluates the weighted averages of the input features to detect any irregularities that could be signs of poisoning efforts. The method finds deviations that can indicate manipulation by adding all the weighted averages and comparing them with the predicted value. Furthermore, it differentiates between binary and multiclass classification instances, accordingly modifying its analysis. The experimental results showed that the VWA algorithm can accurately detect and mitigate data poisoning attacks and improve the robustness and security of machine learning systems against adversarial threats.