User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

Abstract

User behavior clustering analysis has a wide range of applications in business intelligence, information retrieval, and image pattern recognition and fault diagnosis. Most of existing methods of user behavior have some problems such as weak generality and the lack of tags of clustering. With the increasing awareness of privacy protection, user behavior analysis also needs to support for ciphertext to protect user data. Based on clustering algorithm, homomorphic encryption technology and information security, in this paper, we propose a user behavior clustering scheme that supports automatic tags on ciphertext. Firstly, design a security protocol corresponding to the basic operations such as addition, multiplication and comparison and apply to the scheme. Then, the relevant features of the user behavior are merged with the clustering process, the latent factor model, and matrix decomposition. We have implemented our method and evaluated its performance using K-means and K-means++ clustering. The results show that the scheme can auto tags over encrypted data, and the tag also meets the actual situation, which proves the validity and generality of the scheme.

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

“…Random Forests (RF) are one of the most widely used methods, either because they are an established method and included in various machine learning tools [8,27], used as a benchmark [2], or used in an ensemble together with other methods such as in [37]. Clustering variants are also used: k-means [38,39,29,40,41], dbscan [23], c-means [42]. Support Vector Machines (SVM) are also used, sometimes as a single [43] or best classifier in the experiment [29,44] and sometimes to support a claim that another method performs better [45].…”

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

“…Specifically, in the study by Mayhew et al ( 2015 ), K -means++ is used to cluster different categories of machines, such as servers or desktops, as well as web servers or web crawlers. In the study by Gao et al ( 2019 ), the analysis of user behavior clusters is performed by comparing the results obtained using both K -means and K -means++. As an alternative to partitioning-based K -means, fuzzy c-means searches for clusters according to the computation of a data structure (a matrix) that defines the probability that a sample belongs to a certain group.…”

Know Your Enemy: User Segmentation Based on Human Aspects of Information Security

“…Third, we reviewed works that are directly related to both users and security. An experimental research design has been employed in a number of research fields, such as authentication in IoT environments [41], analysis of encrypted data [42], behavior of web users [43], and privacy on social networks [44]. These studies use automatically generated user data as input into user segmentation.…”