在推系统中产生强大的shilling攻击的隐藏干扰的处罚GAN
1Toronto Metropolitan University, Toronto, Canada.
概括
一个新型的惩罚性敌对网络 (PGAN) 有效地为推系统生成现实和多样化的攻击配置文件. 这种方法提高了攻击的稳定性,并且在检测和缓解虚假用户配置文件方面表现优于现有的模型.
科学领域:
- 计算机科学
- 人工智能
- 机器学习
背景情况:
- 通过注入虚假用户配置文件来威胁推者系统的完整性.
- 现有的生成方法与不稳定的培训和不切实际的个人资料产生作斗争.
研究的目的:
- 提出PGAN,一个受惩罚的生成对手网络,用于生成高质量,多样化和不可检测的先令攻击配置文件.
- 通过使用隐藏空间扰动来增强生成攻击配置文件的稳定性和现实性.
主要方法:
- PGAN使用梯度惩罚来稳定歧视者训练.
- 在发电机的潜伏空间中应用受控噪声干扰,以提高稳定性和多样性.
- 评估真实世界数据集使用像击中比率@K和预测转移这样的指标.
主要成果:
- PGAN总是优于传统的统计攻击和基线GAN模型.
- 在MovieLens上获得了0.2051的HR@10评分, 在亚马逊数据集上获得了0.2076的HR@10评分.
- 生成的个人资料与真实用户具有很高的相似性, 证实了他们的现实性.
结论:
- PGAN提供了一种卓越的方法来生成现实和多样化的攻击配置文件.
- 拟议的方法通过提供强大的攻击生成框架来增强推者系统的安全性.
- 与现有的基于网络的攻击方法相比,PGAN是一个显著的进步.
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