Darknet Traffic Classification with Machine Learning Algorithms and SMOTE Method
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Darknet is a network that can be accessed with certain privileges and runs a non-standard communication protocol. The Darknet traffic that consists of data from several known networks such as Tor and the P2P is often used for criminal activities due to its anonymity. It is so critical to correctly classify Darknet traffic to differentiate the individual flows for security purposes. In this paper, we proposed three different machine learning (ML) based traffic classification approaches; the binary classification of Darknet and Benign traffic classes (Case 1); the quadruple classification of classes Tor, NonTor, VPN, and NonVpn (Case 2); an traffic classification of eight sub-traffic classes (Case 3). We further applied the SMOTE method for balancing the sizes of the classes in the traffic dataset and feature selection (FS) algorithms to identify the most effective attributes where the number of features in the original dataset were reduced from 63 to 8, 8 and 6 for Case 1, 2 and 3 respectively. For all three cases, classification was performed with six different machine learning algorithms with and without SMOTE, and the highest accuracy values were obtained with SMOTE method. The highest accuracy values were obtained with the Random Forest Algorithm as 97.22%, 97.16% and 85.99% for Case 1, 2 and 3, respectively. © 2022 IEEE.
Açıklama
7th International Conference on Computer Science and Engineering, UBMK 2022 -- 14 September 2022 through 16 September 2022 -- -- 183844
Anahtar Kelimeler
Cic-Darknet 2020; Darknet; Machine Learning; Traffic Classification, Balancing; Classification (Of Information); Learning Algorithms; Machine Learning; Network Security; Peer To Peer Networks; Cic-Darknet 2020; Communications Protocols; Criminal Activities; Darknets; High-Accuracy; Machine Learning Algorithms; Machine-Learning; Traffic Class; Traffic Classification; Decision Trees
Kaynak
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
WoS Q Değeri
Scopus Q Değeri
N/A
