Data tracking based recommendation system security detection method

Abstract

The invention proposes a data tracking based recommendation system security detection method to overcome the shortcomings of long time for user profile injection, poor attack effect, incapability of adapting to big data processing and the like in conventional collaborative filtering recommendation system detection. According to the method, a score state of a project is tracked and predicted by using a characteristic that extended Kalman filtering (EKF) can be applied to a time nonlinear dynamic system, and then users with abnormal scores in the project are subjected to clustering analysis by utilizing linear discriminant analysis (LDA), so that attack users in the project and the profiles of the users can be determined. With the adoption of the EKF method, the detection of a large amount of unrelated data is reduced, so that the detection efficiency is improved and the system robustness is enhanced. A tracking algorithm is used for recommendation system security detection and can realize online continuous system detection, so that the error detection rate is reduced. The LDA method can perform dimension reduction on multi-characteristic users, so that the profile injection attack of malicious users is effectively detected and the detection rate is increased.

Description

technical field [0001] The invention relates to a data processing method, in particular to a recommendation system security detection method applicable to the field of e-commerce and implemented by using data tracking technology under the background of big data. Background technique [0002] The emergence and popularization of the Internet has brought massive amounts of data to users, satisfying users' needs for information services in the information age. However, with the rapid development of the network, the amount of data has increased significantly, making it impossible for users to obtain the part of information that is really useful to them when faced with a large amount of data. In response to this problem, in order to improve user experience, network service providers have designed or used a collaborative filtering recommendation system, aiming at actively pushing possible useful information to users through the analysis of this system. In today's mobile e-commerce...

AI Technical Summary

Problems solved by technology

[0007] The present invention aims at the defects existing in the recommendation system attack detection method in the prior art, and solves problems such as low detection efficiency and system vulnerability by using technical means such as data tracking

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