Open AccessDeanonymization of users based on correlation analysis
Author(s) -
Nikolay Boldyrikhin,
F A Altunin,
Alexey Svizhenko,
I A Sosnovsky,
I A Yengibaryan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2131/2/022083
Subject(s) - computer science , correlation , order (exchange) , traffic analysis , data mining , computer security , mathematics , economics , geometry , finance
The article discusses the issues of deanonymization of network users who hide their data in order to evade responsibility when committing malicious acts. Within the framework of this work, a user deanonymization algorithm has been developed based on traffic correlation analysis. The algorithm is based on comparing the statistical characteristics of traffic generated by an unknown user with the characteristics of traffic corresponding to known users. An illustrative example of the implementation of the algorithm, which confirms its effectiveness, is given.