A Practical Botnet Traffic Detection System using GNN

Conference Publication ResearchOnline@JCU
Zhang, Bonan;Li, Jingjin;Chen, Chao;Lee, Kyungmi;Lee, Ickjai
Kyungmi Joanne LeeIckjai Lee
Abstract

Botnet attacks have now become a major source of cyber-attacks. How to detect botnet traffic quickly and efficiently is a current problem for most enterprises. To solve this, we have built a plug-and-play botnet detection system using graph neural network algorithms. The system performs very well in detecting botnets of different structures. The system is also made with a graphical interface to visualise which nodes are at risk of botnets. The system is also very efficient in identifying botnet traffic.

Journal

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Publication Name

CSS 2021: Cyberspace Safety and Security

Volume

13172

ISBN/ISSN

978-3-030-94029-4

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Issue

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Pages Count

13

Location

Virtual

Publisher

Springer

Publisher Url

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Publisher Location

Cham, Switzerland

Publish Date

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Url

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Date

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EISSN

N/A

DOI

10.1007/978-3-030-94029-4_5