Collaborative recommender systems 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 design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system's recommendation behavior. Prior work has shown when profiles are reverse engineered to maximize influence; even a small number of malicious profiles can significantly bias the system. This paper describes a classification approach to the problem of detecting and responding to profile injection attacks. A number of attributes are identified that distinguish characteristics present in attack profiles in general, as well as an attribute generation approach for detecting profiles based on reverse engineered attack models. Three well-known classification algorithms are then used to demonstrate the combined benefit of these attributes and the impact the selection of classifier has with respect to improving the robustness of the recommender system. Our study demonstrates this technique significantly reduces the impact of the most powerful attack models previously studied, particularly when combined with a support vector machine classifier.

SUMMARY1. Adult rat heart cells were isolated enzymically and ATP was identified in the cell suspension using the firefly luminescence technique. Adenosine 5'-triphosphate (ATP) was not detected from cell suspensions obtained from hearts which had been left asystolic for 10 min. 4am/min at 370 C. Q10 was found to be 4 between 25 and 370 C. Enzyme activity remained unaffected by either hypoxic conditions or ouabain.5. If these amounts of ATP are released from myocardial cells rendered hypoxic in vivo, then it must be concluded that ATP plays a principal role in the local control of myocardial blood flow.6. It is proposed that release of ATP occurs through the sarcolemma from an intracellular pool, and that alteration of the configuration of structural membrane protein controls the amounts of ATP extruded.

No abstract

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system's recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system's responses to these users. We show that such attacks are both pragmatically reasonable and also highly effective against both user-based and itembased algorithms. As a result, an attacker can mount such a "segmented" attack with little knowledge of the specific system being targeted and with strong likelihood of success.