An Obfuscated Attack Detection Approach for Collaborative Recommender Systems
نویسندگان
چکیده
منابع مشابه
Detection of Obfuscated Attacks in Collaborative Recommender Systems
The vulnerability of collaborative recommender systems has been well established; particularly to reverse-engineered attacks designed to bias the system in an attacker’s favor. Recent research has begun to examine detection schemes to recognize and defeat the effects of known attack models. In this paper we propose several techniques an attacker might use to modify an attack to avoid detection,...
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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 filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too “dull” to handle such imbalanced classification. In this paper, we improve dete...
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Recommender systems use ratings from users on items such as movies and music for the purpose of predicting the user preferences on items that have not been rated. Predictions are normally done by using the ratings of other users of the system, by learning the user preference as a function of the features of the items or by a combination of both these methods. In this paper, we pose the problem ...
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ژورنال
عنوان ژورنال: Journal of Computing and Information Technology
سال: 2018
ISSN: 1330-1136,1846-3908
DOI: 10.20532/cit.2018.1003948