论文标题
使用顺序gan检测对建议系统的数据污染攻击
On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs
论文作者
论文摘要
推荐系统是任何电子商务平台的重要组成部分。建议通常通过汇总大量用户数据来生成。恶意演员可能会通过注入恶意数据点来利用系统以获取财务收益来摇摆此类推荐系统的输出。在这项工作中，我们提出了一种半监督攻击检测算法，以识别恶意数据点。我们通过利用数据集的一部分来做到这一点，该数据集被污染的机会较低以了解真实数据点的分布。我们提出的方法修改了生成的对抗网络体系结构，以考虑从用户活动中的上下文信息。这使该模型可以将合法数据点与注入的数据区分开。
Recommender systems are an essential part of any e-commerce platform. Recommendations are typically generated by aggregating large amounts of user data. A malicious actor may be motivated to sway the output of such recommender systems by injecting malicious datapoints to leverage the system for financial gain. In this work, we propose a semi-supervised attack detection algorithm to identify the malicious datapoints. We do this by leveraging a portion of the dataset that has a lower chance of being polluted to learn the distribution of genuine datapoints. Our proposed approach modifies the Generative Adversarial Network architecture to take into account the contextual information from user activity. This allows the model to distinguish legitimate datapoints from the injected ones.
