期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2014
卷号：8
期号：4
页码：253-264
DOI：10.14257/ijsia.2014.8.4.23
出版社：SERSC
摘要：In view of the high vulnerability of traditional user-based recommendation algorithm to shilling attacks, In this paper, on the basis of the work of the group effect on the attack profiles, this paper analyzes the statistical features of the nearest neighbors of target users before and after attack, Design a kind of Attack Profiles online filter to attack the target user profile from the nearest neighbor filter. And this filter improves the user-based recommendation algorithm nearest neighbor selection strategy, thus proposes the Collaborative Recommendation algorithm based on Online Filter for Attack Profiles (CROFAP). Experiments show that attack profile online filter can accurately identify and filter out most attacks profile to ensure the robustness of the CROFAP algorithm.
