Observing Consistency in Online Communication Patterns for User Re-Identification

被引：13

作者：

Adeyemi, Ikuesan Richard ^{[1
,2
]}

Razak, Shukor Abd ^{[1
]}

Salleh, Mazleena ^{[1
]}

Venter, Hein S. ^{[2
]}

机构：

[1] Univ Teknol Malaysia, Fac Comp, Informat Assurance & Secur Res Grp, Skudai, Johor Bahru, Malaysia

[2] Univ Pretoria, Informat & Comp Secur Architecture Res Grp, Dept Comp Sci, Lynnwood, South Africa

来源：

PLOS ONE | 2016年 / 11卷 / 12期

关键词：

TIME;

D O I：

10.1371/journal.pone.0166930

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent inter-connectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas.

引用

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