Detecting profile injection attacks in collaborative filtering: a classification-based approach
作者： Chad A. Williams , Bamshad Mobasher , Robin Burke , Runa Bhaumik
DOI: 10.1007/978-3-540-77485-3_10
关键词: Injection attacks 、 Collaborative filtering 、 Robustness (computer science) 、 Recommender system 、 Information gain 、 Data mining 、 Computer science 、 Classifier (UML)
摘要: Collaborative recommender systems have been shown to be vulnerable profile injection attacks. By injecting a large number of biased profiles into system, attackers can manipulate the predictions targeted items. To decrease this risk, researchers begun study mechanisms for detecting and preventing In prior work, we proposed several attributes attack detection that classifier built with them highly successful at identifying profiles. paper, extend our work through more detailed analysis information gain associated these across dimensions type size. We then evaluate their combined effectiveness improving robustness user based systems.
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