Profile Injection Attack Detection in Recommender System
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چکیده
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منابع مشابه
Profile Injection Attack Detection for Securing Collaborative Recommender Systems
Researchers have shown that collaborative recommender systems, the most common type of web personalization system, are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to...
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Collaborative recommender systems have been shown to be vulnerable to profile injection attacks. By injecting a large number of biased profiles into a system, attackers can manipulate the predictions of targeted items. To decrease this risk, researchers have begun to study mechanisms for detecting and preventing profile injection attacks. In prior work, we proposed several attributes for attack...
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Significant vulnerabilities have been identified in collaborative recommender systems. The open nature of collaborative filtering allows attackers to inject biased profile data and force the system to “adapt” in a manner advantageous to them. Previous work has shown both user-based and item-based recommender systems are vulnerable to the segment attack model. In this paper we focus on two techn...
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Recent research has shown the significant vulnerabilities of collaborative recommender systems in the face of profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the system’s output. A single Support Vector Machine (SVM) approach for the detection of profile injection attacks, however, suffers from low precision. With this problem i...
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