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With the development of information technology and the Internet,recommendation systems have become an important means to solve the problem ofinformation overload. However, recommendation system is greatly fragile as itrelies heavily on behavior data of users, which makes it very easy for a hostof malicious merchants to inject shilling attacks in order to manipulate therecommendation results. Some papers on shilling attack have proposed thedetection methods, whether based on false user profiles or abnormal items, buttheir detection rate, false alarm rate, universality, and time overhead need tobe further improved. In this paper, we propose a new item anomaly detectionmethod, through T-distribution technology based on Dynamic Time Intervals.First of all, based on the characteristics of shilling attack quickness(Attackers inject a large number of fake profiles in a short period in order tosave costs), we use dynamic time interval method to divide the rating historyof item into multiple time windows. Then, we use the T-distribution to detectthe exception windows. By conducting extensive experiments on a dataset thataccords with real-life situations and comparing it to currently outstandingmethods, our proposed approach has a higher detection rate, lower false alarmrate and smaller time overhead to the different attack models and filler sizes.

Detection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems