Recommendation system attack detection method based on time series data

Abstract

The invention discloses a recommendation system attack detection method based on time series data. The method includes the steps that a user-project preference degree data set and a frequent item set excavation technology are utilized to acquire user groups and project groups; group preference degree value proportional characteristics are calculated for the pairs of user sets and the pairs of project sets; all preference degrees of projects in the project groups form time series preference degree data according to operating time; group preference degree time interval characteristics are calculated for the pairs of user sets and the project sets; group average entropy characteristics are calculated for the user groups; for the user groups, maximum group preference degree value proportional characteristics corresponding to the user groups and maximum group preference degree time interval characteristics corresponding to the user groups are selected, and the user groups are sequentially ranked through the three characteristics, and then three ordered user group sequences are acquired; the three ordered user group sequences are integrated to acquire a wholly ordered user group sequence, and the most probably attack user groups are acquired; the most probably target project groups are acquired through the group preference degree value proportional characteristics.

Description

technical field [0001] The invention relates to the fields of machine learning and pattern recognition, in particular to the problem of attack detection of recommendation systems based on collaborative filtering in machine learning. Background technique [0002] In recent years, with the rapid development of the network, people are faced with a large amount of information every day. Faced with thousands of information, people are tired of finding the valuable information they are interested in. The emergence of recommendation system can free people from the massive information. The recommendation system is an information filtering technology, which can filter out valuable content that users are interested in from a large amount of information and provide it to users, so that users can be freed from complicated information. Commonly used recommendation system technologies include content-based recommendation systems, collaborative filtering-based recommendation systems, and ...

AI Technical Summary

Problems solved by technology

Although this kind of group attack behavior is widespread in practice, there are few works on how to detect group attack behavior, that is, to detect both the attacking user group and the target project group attacked by the attacking user group.

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