Machine Learning Approaches for Botnet Detection in Network Traffic-Reference-Cited by-同舟云学术

Machine Learning Approaches for Botnet Detection in Network Traffic

Published:2024-05-25 Issue: Volume:10 Page:310-315
ISSN:
Container-title:Proceedings of the Cognitive Models and Artificial Intelligence Conference
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Author:

Salih Yousif Tareq¹^ORCID,Fenjan Ali²^ORCID,Ahmed Saadaldeen Rashid³^ORCID,Ali Hussein⁴^ORCID,Abdulwahab Emad.N⁵^ORCID,Algruri Sameer⁶^ORCID,Kurdi Neesrin Ali⁷^ORCID,Al-Sarem Mohammed⁷^ORCID,Tawfeq Jamal Fadhil⁸^ORCID

Affiliation:

1. Al-Maarif University, Iraq

2. American International University, Kuwait

3. Al-Ayen University, Bayan University, Iraq

4. Al Hikma University, Afghanistan

5. Al-Ma moon University, Afghanistan

6. Al-Kitab University, Iraq

7. Taibah University, Afghanistan

8. Al-Farahidi University, Afghanistan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660853.3660933

Reference33 articles.

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2. Botnet detection based on network behavior;Strayer W. T.;Botnet Detection: Countering the Largest Security Threat,2008

3. Dong, X., Hu, J., & Cui, Y. (2018, September). Overview of botnet detection based on machine learning. In 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) (pp. 476-479). IEEE.

4. Park, Y., Kengalahalli, N. V., & Chang, S. Y. (2018, November). Distributed security network functions against botnet attacks in software-defined networks. In 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) (pp. 1-7). IEEE.

5. Botnet Detection System Analysis on the Effect of Botnet Evolution and Feature Representation