A mobile user abnormal behavior detection method

Abstract

In order to overcome the problem in the prior art that the selection of cluster members is likely to cause a high rate of false alarms in detection results, the present invention provides a mobile user abnormal behavior detection method, which uses a sliding window to dynamically acquire data to improve the accuracy of user behavior acquisition. Accuracy, the concept of Duun_index is introduced after the initial clustering and incremental stages of the traditional FC algorithm, the cluster members generated after the increment are selected, and then the selected high-quality members are fused with the voting algorithm to obtain the final result, and then combined with When the user's normal behavior is compared with the similarity, the correlation matrix is ​​introduced, and the change of the average difference is used to judge whether the user's behavior is normal, so as to achieve the purpose of efficient and accurate anomaly detection.

Description

technical field [0001] The invention relates to the field of information security and trustworthy technology, in particular to a method for detecting abnormal behavior of mobile users based on selective clustering and fusion. Background technique [0002] With the wide application of the Internet, the life and work of the entire human society are gradually being influenced and changed by computer technology, network technology and communication technology. With the rapid popularization of smart terminals and the rapid development of mobile Internet, many users have transferred their Internet access from PCs to mobile smart terminals such as smart phones. The application of cloud computing technology in the mobile communication industry will inevitably create a new era of mobile Internet. At present, the security and other trustworthiness requirements involved in mobile cloud services are mostly relatively low, and the credibility of various elements and links involved in mob...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problem in the prior art that the selection of cluster members is likely to cause a high rate of false alarms in detection results, the present invention provides a mobile user abnormal behavior detection method, which uses a sliding window to dynamically acquire data to improve the accuracy of user behavior acquisition. Accuracy, the concept of Duun_index is introduced after the initial clustering and incremental stages of the traditional FC algorithm, the cluster members generated after the increment are selected, and then the selected high-quality members are fused with the voting algorithm to obtain the final result, and then combined with The correlation matrix is ​​introduced when the user's normal behavior is compared with the similarity, and the change of the average difference is used to judge whether the user's behavior is normal, so as to achieve the purpose of efficient and accurate anomaly detection

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