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Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm

Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm

Sangita Baruah, Dhruba Jyoti Borah, Vaskar Deka
Copyright: © 2023 |Volume: 17 |Issue: 1 |Pages: 16
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781668479131|DOI: 10.4018/IJISP.319303
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MLA

Baruah, Sangita, et al. "Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm." IJISP vol.17, no.1 2023: pp.1-16. http://doi.org/10.4018/IJISP.319303

APA

Baruah, S., Borah, D. J., & Deka, V. (2023). Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm. International Journal of Information Security and Privacy (IJISP), 17(1), 1-16. http://doi.org/10.4018/IJISP.319303

Chicago

Baruah, Sangita, Dhruba Jyoti Borah, and Vaskar Deka. "Detection of Peer-to-Peer Botnet Using Machine Learning Techniques and Ensemble Learning Algorithm," International Journal of Information Security and Privacy (IJISP) 17, no.1: 1-16. http://doi.org/10.4018/IJISP.319303

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Abstract

Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and F1-score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% F1 score, which outperform the previous botnet detection approaches.