Data tracking based recommendation system security detection method

A recommendation system and security detection technology, applied in the field of data processing, can solve problems such as low detection efficiency and system vulnerability, and achieve the effect of reducing error rate, avoiding invalidity or inefficiency, and overcoming low efficiency

Active Publication Date: 2016-07-27
NANJING UNIV OF POSTS & TELECOMM
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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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  • Data tracking based recommendation system security detection method
  • Data tracking based recommendation system security detection method
  • Data tracking based recommendation system security detection method

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Embodiment Construction

[0062] A specific embodiment is provided below to apply the detection method of the present invention to a real e-commerce scene. Here, the MovieLens dataset is used as the rating database of the recommendation system for experiments. The dataset is provided by the GroupLens research team of the University of Minnesota in the United States. It contains 100,000 rating records of 1,682 movies from 943 users, so it has rich attributes, The advantages of data authenticity are widely used in data mining and other fields. The comparative detection methods used in the present invention include SVM and UnRAP, both of which are relatively popular detection methods in current research and application. The present invention adopts the common random attack and popular attack in the recommendation system to attack the method of the present invention and the SVM and UnRAP methods. The attack scales (AttackSize) of these two types of attack methods are respectively 3% and 15%. Through the co...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/55
CPCG06F21/55
Inventor 黄海平李峰朱洁叶宁王鹏王汝传沙超吴鹏飞
Owner NANJING UNIV OF POSTS & TELECOMM
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