  "Shilling" attacks or "profile injection" attacks have always major
challenges in collaborative filtering recommender systems (CFRSs). Many efforts
have been devoted to improve collaborative filtering techniques which can
eliminate the "shilling" attacks. However, most of them focused on detecting
push attack or nuke attack which is rated with the highest score or lowest
score on the target items. Few pay attention to grey attack when a target item
is rated with a lower or higher score than the average score, which shows a
more hidden rating behavior than push or nuke attack. In this paper, we present
a novel detection method to make recommender systems resistant to such attacks.
To characterize grey ratings, we exploit rating deviation of item to
discriminate between grey attack profiles and genuine profiles. In addition, we
also employ novelty and popularity of item to construct rating series. Since it
is difficult to discriminate between the rating series of attacker and genuine
users, we incorporate into discrete wavelet transform (DWT) to amplify these
differences based on the rating series of rating deviation, novelty and
popularity, respectively. Finally, we respectively extract features from rating
series of rating deviation-based, novelty-based and popularity-based by using
amplitude domain analysis method and combine all clustered results as our
detection results. We conduct a list of experiments on both the Book-Crossing
and HetRec-2011 datasets in diverse attack models. Experimental results were
included to validate the effectiveness of our approach in comparison with the
benchmarked methods.
