Attack detection in time series for recommender systems

Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recommendations, have been shown to be effective against collaborative filtering algorithms. We postulate that the distribution of item ratings in time can reveal the presence of a wide range of shilling attacks given reasonable assumptions about their duration. To construct a time series of ratings for an item, we use a window size of k to group consecutive ratings for the item into disjoint windows and compute the sample average and sample entropy in each window. We derive a theoretically optimal window size to best detect an attack event if the number of attack profiles is known. For practical applications where this number is unknown, we propose a heuristic algorithm that adaptively changes the window size. Our experimental results demonstrate that monitoring rating distributions in time series is an effective approach for detecting shilling attacks.