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

[21] Lithuanian river ice detection and automated classification using machine-learning methods
Bevainis, Linas
Bielinis, Martynas
Cesnulevicius, Agimantas
Bautrenas, Arturas
BALTICA, 2023, 36 (01): : 1 - 12
[22] A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning
Hashimoto, Naoki
Ozawa, Seiichi
Ban, Tao
Nakazato, Junji
Shimamura, Jumpei
INNS CONFERENCE ON BIG DATA AND DEEP LEARNING, 2018, 144 : 118 - 123
[23] Darknet traffic analysis, and classification system based on modified stacking ensemble learning algorithms
Almomani, Ammar
INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT, 2023,
[24] Classification of Space Objects Using Machine Learning Methods
Khalil, Mahmoud
Fantino, Elena
Liatsis, Panos
2019 IEEE FIRST INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE (COGMI 2019), 2019, : 93 - 96
[25] Classification of Urine Odour Using Machine Learning Methods
Xing, Yuxin
Gardner, Julian W.
2022 IEEE INTERNATIONAL SYMPOSIUM ON OLFACTION AND ELECTRONIC NOSE (ISOEN 2022), 2022,
[26] CIN Classification and Prediction Using Machine Learning Methods
Chirkina, Anastasia
Medvedeva, Marina
Komotskiy, Evgeny
APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2017, 1836
[27] Classification of ECG Signal by using Machine Learning Methods
Diker, Aykut
Avci, Engin
Comert, Zafer
Avci, Derya
Kacar, Emine
Serhatlioglu, Ihsan
2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2018,
[28] A Novel Darknet Traffic Classification Method based on Knowledge Graph with Dynamic Embedding Learning
Hu, Xiaoyan
Li, Ning
Cheng, Guang
Li, Ruidong
Wu, Hua
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 3799 - 3804
[29] Traffic sign segmentation and classification using statistical learning methods
Lillo-Castellano, J. M.
Mora-Jimenez, I.
Figuera-Pozuelo, C.
Rojo-Alvarez, J. L.
NEUROCOMPUTING, 2015, 153 : 286 - 299
[30] Real-Time Traffic Sign Detection and Classification Using Machine Learning and Optical Character Recognition
Ciuntu, Victor
Ferdowsi, Hasan
2020 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2020, : 480 - 486

← 1 2 3 4 5 →