Shilling Attack Detection Based on Feature Selection for Recommendation Systems
Jie Ming Cao · Dianzi xuebao · 2012
Most of the e-business recommender systems are based upon collaborative filtering(CF) algorithms.Since such systems have been shown to be vulnerable to shilling attacks in which malicious user profiles are inserted into the system in order to push or nuke the predictions of some targeted items,shilling attack detection has recently become a hot research topic in recommender systems.Firstly,the effectiveness of five types of attacks against different CF algorithms is analyzed.Secondly,a feature selection algorithm is presented.Two kinds of shilling attack detection algorithms based on supervised learning are then proposed:the first one is based on nave Bayesian classifier,and the second one is based on k nearest neighbor(kNN) classifier.At last,experimental results show the effectiveness of the feature selection algorithm and the sensitivity and specificity of these two kinds of detection algorithms.