Detection and classification of darknet traffic using machine learning methods

被引：3

作者：

Ugurlu, Mesut ^{[1
]}

Dogru, Ibrahim Alper ^{[2
]}

Arslan, Recep Sinan ^{[3
]}

机构：

[1] Gazi Univ, Grad Sch Nat & Appl Sci, Dept Informat Secur Engn, TR-06570 Ankara, Turkiye

[2] Gazi Univ, Fac Technol, Dept Comp Engn, TR-06570 Ankara, Turkiye

[3] Kayseri Univ, Fac Engn, Dept Comp Engn, TR-38039 Kayseri, Turkiye

来源：

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY | 2023年 / 38卷 / 03期

关键词：

Darknet; Cyber security; Encrypted network traffic; Machine learning; Classification; FEATURE-SELECTION;

D O I：

10.17341/gazimmfd.1023147

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Graphical/Tabular In this study, a machine learning-based model has been developed for the detection and classification of the darknet or dark web that cybercriminals and attackers use to hide their identity information and provide encrypted communication. The statistical information of packets was analyzed using machine learning approach without deciphering encrypted network traffic. Feature selection was made to increase the performance of the model. In addition to this process, data balancing was performed in order to increase the detection and classification rate of features with low numbers during the training phase. The created model is given in Figure A.

引用

页码：1737 / 1746

页数：10

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