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

共 50 条

[41] Botnet Detection on TCP Traffic Using Supervised Machine Learning
Velasco-Mata, Javier
Fidalgo, Eduardo
Gonzalez-Castro, Victor
Alegre, Enrique
Blanco-Medina, Pablo
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019, 2019, 11734 : 444 - 455
[42] Detection of Encrypted Malicious Network Traffic using Machine Learning
De Lucia, Michael J.
Cotton, Chase
MILCOM 2019 - 2019 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM), 2019,
[43] Dark Web Traffic Detection Using Supervised Machine Learning
Nezhad, Sahra Zangeneh
Baniasadi, Amirali
2023 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, CCECE, 2023,
[44] Anomaly detection in network traffic using extreme learning machine
Imamverdiyev, Yadigar
Sukhostat, Lyudmila
2016 IEEE 10TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT), 2016, : 418 - 421
[45] Network Traffic Anomaly Detection using Machine Learning Approaches
Limthong, Kriangkrai
Tawsook, Thidarat
2012 IEEE NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (NOMS), 2012, : 542 - 545
[46] Flow Based Botnet Traffic Detection Using Machine Learning
Gahelot, Parul
Dayal, Neelam
PROCEEDINGS OF ICETIT 2019: EMERGING TRENDS IN INFORMATION TECHNOLOGY, 2020, 605 : 418 - 426
[47] Traffic Data Classification using Machine Learning Algorithms in SDN Networks
Kwon, Jungmin
Jung, Daeun
Park, Hyunggon
11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020), 2020, : 1031 - 1033
[48] Intelligent Classification of IoT Traffic in Healthcare Using Machine Learning Techniques
Panda, Sashmita
Panda, Ganapati
2020 6TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2020, : 581 - 585
[49] A new classification method for encrypted internet traffic using machine learning
Ugurlu, Mesut
Dogru, Ibrahim Alper
Arslan, Recep Sinan
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2021, 29 (05) : 2450 - 2468
[50] QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
Almuhammadi, Sultan
Alnajim, Abdullatif
Ayub, Mohammed
APPLIED SCIENCES-BASEL, 2023, 13 (08):

← 1 2 3 4 5 →