Detecting Botnet Victims using ML
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 9
Abstract
Botnets are one of the most devasting cybersecurity threats to modern organizations. A botnet is a distributed network of compromised devices that is leveraged to perform various activities related to malicious operations over the internet. Machine learning techniques are capable of detecting the compromised hosts (bot victims) operating on a network. The advantage of our approach is that a bot victim can be detected not only through its actions but also through the actions of the devices it communicates with; an intrinsic characteristic of botnet activity. Network traffic information can usually be easily retrieved from various network devices without affecting significantly network performance or service availability. We study the feasibility of detecting botnet activity without having seen a complete network flow by classifying behavior based on time intervals. Identification of compromised devices is done. Using existing datasets, we show experimentally that it is possible to identify the presence of existing and unknown botnets activity with high accuracy even with very small-time windows.
Authors and Affiliations
Y Nagendra Kumar, P. N. L. Sravani, Md. Chahitha, A. Geetha Nandini, S. Srivalli
Keywords
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- EP ID EP747888
- DOI https://doi.org/10.46501/IJMTST1009007
- Views 30
- Downloads 0
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