  With the development of information technology and the Internet,
recommendation systems have become an important means to solve the problem of
information overload. However, recommendation system is greatly fragile as it
relies heavily on behavior data of users, which makes it very easy for a host
of malicious merchants to inject shilling attacks in order to manipulate the
recommendation results. Some papers on shilling attack have proposed the
detection methods, whether based on false user profiles or abnormal items, but
their detection rate, false alarm rate, universality, and time overhead need to
be further improved. In this paper, we propose a new item anomaly detection
method, 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 to
save costs), we use dynamic time interval method to divide the rating history
of item into multiple time windows. Then, we use the T-distribution to detect
the exception windows. By conducting extensive experiments on a dataset that
accords with real-life situations and comparing it to currently outstanding
methods, our proposed approach has a higher detection rate, lower false alarm
rate and smaller time overhead to the different attack models and filler sizes.
