BotCB: Unmasking Botnets Through Intelligent Network Traffic Analysis.
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| Title: | BotCB: Unmasking Botnets Through Intelligent Network Traffic Analysis. |
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| Authors: | Sivanesh, S.1 (AUTHOR) sivanesh.s@ucep.edu.in, Mani, G.2 (AUTHOR), Senthilkumar, D.3 (AUTHOR), Serrano, Salvatore (AUTHOR) sserrano@unime.it |
| Source: | International Journal of Distributed Sensor Networks. 10/7/2025, Vol. 2025, p1-9. 9p. |
| Subjects: | Botnets, Random forest algorithms, Classification algorithms, Computer network security, Machine learning, Computer network traffic |
| Abstract: | Botnet attacks continue to be a serious threat to network security, demanding the creation of reliable and effective detection solutions. This paper introduces a comprehensive botnet detection system to analyse network traffic (NT) flow using machine learning techniques. BotCB analyses NT patterns and automatically detects botnet assaults by incorporating classification techniques, such as logistic regression, random forest, naive Bayes and decision tree. The system architecture of BotCB incorporates a data collection module aided by third‐party dependencies such as tcpdump and argus, a preprocessing module, a training and evaluation module and a user interface. Extensive experiments are conducted using the CTU‐13 dataset—a widely used dataset including various NT scenarios—to analyse and compare the effectiveness of the various classification methods. The Random Forest algorithm classifies botnet activities with an astounding 99.99% accuracy rate when compared with the other algorithm in the test. BotCB assists network administrators and security professionals in proactively identifying malicious IP addresses in order to mitigate botnet infections. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Botnet attacks continue to be a serious threat to network security, demanding the creation of reliable and effective detection solutions. This paper introduces a comprehensive botnet detection system to analyse network traffic (NT) flow using machine learning techniques. BotCB analyses NT patterns and automatically detects botnet assaults by incorporating classification techniques, such as logistic regression, random forest, naive Bayes and decision tree. The system architecture of BotCB incorporates a data collection module aided by third‐party dependencies such as tcpdump and argus, a preprocessing module, a training and evaluation module and a user interface. Extensive experiments are conducted using the CTU‐13 dataset—a widely used dataset including various NT scenarios—to analyse and compare the effectiveness of the various classification methods. The Random Forest algorithm classifies botnet activities with an astounding 99.99% accuracy rate when compared with the other algorithm in the test. BotCB assists network administrators and security professionals in proactively identifying malicious IP addresses in order to mitigate botnet infections. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15501329 |
| DOI: | 10.1155/dsn/2344785 |
