Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems
Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems
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发表时间：
2005
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通讯作者：
B. Mobasher;R. Burke;Runa Bhaumik;Chad Williams
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作者：
B. Mobasher;R. Burke;Runa Bhaumik;Chad Williams
协同过滤推荐系统中的重要漏洞最近被发现。这些漏洞主要源于这种系统的开放性，以及它们对用户对建筑轮廓的特定判断的依赖。无法轻易区分普通用户的攻击者可能会引入有偏见的数据，试图迫使系统以有利于他们的方式“适应”。到目前为止，已经确定了一些简单的攻击模型，不同的推荐技术对这些攻击的敏感性似乎存在显着差异。特别是，基于项目的协同过滤已被发现提供一些安全优势，基于用户的协同过滤。我们在安全个性化方面的研究正在研究一系列更复杂的攻击模型和推荐技术，特别关注发起攻击的成本和收益。在本文中，我们将仔细研究基于项目的协同过滤。特别是，我们提出了一个新的攻击模型，专注于一个子集的用户具有相似的口味，并表明这样的攻击可以非常成功地对基于项目的算法。
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers who cannot be readily distinguished from ordinary users may introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. A handful of simple attack models have, so far, been identified, and there appear to be significant differences in the susceptibility of different recommendation techniques to these attacks. In particular, item-based collaborative filtering has been found to offer some security advantages over user-based collaborative filtering. Our research in secure personalization is examining a range of more complex attack models and recommendation techniques, paying particular attention to the costs and benefits of mounting an attack. In this paper, we take a closer look at item-based collaborative filtering. In particular, we propose a new attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against an item-based algorithm.