Machine Learning Based Botnet Detection

Authors

  • Shubham Gour  Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Yogesh Bhosle  Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Onkar Jagtap  Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Pratik Nirmale  Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Prof. Monika Dangore  Professor, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India

Keywords:

Botnet, XGBoost, NaiveBayes, DDOS, Decision Tree, Random Forest, Network Traffic.

Abstract

Botnet term was coined when multiple networks of bots came into existence. It is a number of Internet-connected devices, which run single or multiple bots. Botnets can be used to perform Distributed Denial-of-Service attacks, sending spams, and allowing attackers to gain unauthorised access on connections Command and control software is used by the Owner (BotMaster) to control the botnet. This paper discusses the accuracy of the prediction of Botnet detection using different models.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shubham Gour, Yogesh Bhosle, Onkar Jagtap, Pratik Nirmale, Prof. Monika Dangore, " Machine Learning Based Botnet Detection" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.101-107, May-June-2021.