标题： 防御机器学习模型中的Beta中毒攻击 显示英文标题

标题： Defending Against Beta Poisoning Attacks in Machine Learning Models

摘要： 针对机器学习（ML）模型训练数据集的对抗性操纵的中毒攻击，对ML安全构成重大威胁。 Beta中毒是一种最近提出的中毒攻击，通过使训练数据集线性不可分来破坏模型准确性。 在本文中，我们提出了四种针对Beta中毒攻击的防御策略：基于kNN邻近度的防御（KPB），邻域类别比较（NCC），基于聚类的防御（CBD）和均值距离阈值（MDT）。 这些防御措施基于我们对由Beta中毒生成的中毒样本特征的观察，例如，中毒样本彼此接近，并且集中在目标类别的均值附近。 使用MNIST和CIFAR-10数据集的实验评估表明，KPB和MDT可以实现完美的准确率和F1分数，而CBD和NCC也提供了强大的防御能力。 此外，通过分析不同参数下的性能，我们提供了关于防御在不同条件下行为的实际见解。 显示英文摘要

摘要： Poisoning attacks, in which an attacker adversarially manipulates the training dataset of a machine learning (ML) model, pose a significant threat to ML security. Beta Poisoning is a recently proposed poisoning attack that disrupts model accuracy by making the training dataset linearly nonseparable. In this paper, we propose four defense strategies against Beta Poisoning attacks: kNN Proximity-Based Defense (KPB), Neighborhood Class Comparison (NCC), Clustering-Based Defense (CBD), and Mean Distance Threshold (MDT). The defenses are based on our observations regarding the characteristics of poisoning samples generated by Beta Poisoning, e.g., poisoning samples have close proximity to one another, and they are centered near the mean of the target class. Experimental evaluations using MNIST and CIFAR-10 datasets demonstrate that KPB and MDT can achieve perfect accuracy and F1 scores, while CBD and NCC also provide strong defensive capabilities. Furthermore, by analyzing performance across varying parameters, we offer practical insights regarding defenses' behaviors under varying conditions.