Detection and classification of darknet traffic using machine learning methods Karanlik aǧ trafiǧinin makine öǧrenmesi yöntemleri kullanilarak tespiti ve siniflandirilmasi

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Uǧurlu M., DOĞRU İ. A., ARSLAN R. S.

Journal of the Faculty of Engineering and Architecture of Gazi University, vol.38, no.3, pp.1737-1746, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.17341/gazimmfd.1023147
  • Journal Name: Journal of the Faculty of Engineering and Architecture of Gazi University
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1737-1746
  • Keywords: classification, cyber security, Darknet, encrypted network traffic, machine learning
  • Kayseri University Affiliated: Yes


© 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.With digitalization, the world of crime has also become digital and the number of crimes committed over the internet is increasing day by day. Cybercriminals and attackers use secret networks on the Internet, called the Dark Web, to hide their identities and provide encrypted communication. Darknets have different and special access methods than normal internet infrastructure. All access to these networks is suspect and needs to be investigated. Because the Darknet provides encrypted communication, it is difficult to detect and classify with today's security tools. In this study, only the statistical information of packets was analyzed using machine learning approach without deciphering encrypted network traffic. CICDarknet2020 dataset was used and a detailed experimental study including K Nearest Neighbor, Logistic Regression, Random Forest, SVM, Decision Tree, Gaussian Naive Bayes, Linear Discriminatory Analysis, Gradient Boosting, Extra Tree and XGBoost algorithms was carried out for packet analysis. In experimental studies, it has been observed that the Decision Tree algorithm has the highest classification success with an accuracy rate of 93.32%.