  Personalization collaborative filtering recommender systems (CFRSs) are the
crucial components of popular e-commerce services. In practice, CFRSs are also
particularly vulnerable to "shilling" attacks or "profile injection" attacks
due to their openness. The attackers can carefully inject chosen attack
profiles into CFRSs in order to bias the recommendation results to their
benefits. To reduce this risk, various detection techniques have been proposed
to detect such attacks, which use diverse features extracted from user
profiles. However, relying on limited features to improve the detection
performance is difficult seemingly, since the existing features can not fully
characterize the attack profiles and genuine profiles. In this paper, we
propose a novel detection method to make recommender systems resistant to the
"shilling" attacks or "profile injection" attacks. The existing features can be
briefly summarized as two aspects including rating behavior based and item
distribution based. We firstly formulate the problem as finding a mapping model
between rating behavior and item distribution by exploiting the least-squares
approximate solution. Based on the trained model, we design a detector by
employing a regressor to detect such attacks. Extensive experiments on both the
MovieLens-100K and MovieLens-ml-latest-small datasets examine the effectiveness
of our proposed detection method. Experimental results were included to
validate the outperformance of our approach in comparison with benchmarked
method including KNN.
