Unsupervised Detection of Shilling Attack for Recommender System Based on Feature Subset
Peng Fe · Jisuanji gongcheng · 2014
To solve the problem that existing recommender systems based on collaborative filtering are vulnerable to the shilling attack, this paper proposes an Unsupervised Detection Algorithm of Shilling Attack Based on Feature Subset(UnDSA-FS). A feature named Kurtosis Coefficient of Interest(KCI) is proposed to describe the intensity degree of user's interest. Taking the KCI and other existed features as candidate feature set, this algorithm uses unsupervised feature selection method to choose proper feature subset for different attack strategies. It computes the distance sum of each user, sorts the users by the distance sum and identifies the attack target. It sets a sliding window on the sorted user sequence, and filters the attack users by calculating the mean rating deviation of attack target. Experimental result verifies that the information gain of KCI is higher than existing features', and the proposed UnDSA-FS has a better performance in stability and precision compared with existing unsupervised detection methods.