document: 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 .
doc_bio_tags: O O O B I O B I O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O