Zhang, Bonan, Li, Jingjin, Chen, Chao, Lee, Kyungmi, and Lee, Ickjai (2022) A Practical Botnet Traffic Detection System using GNN. In: Lecture Notes in Computer Science (13172) pp. 66-78. From: CSS 2021: Cyberspace Safety and Security, 9-11 November 2021, Virtual.

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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.

Item ID:	73112
Item Type:	Conference Item (Research - E1)
ISBN:	978-3-030-94029-4
Copyright Information:	© 2022 Springer Nature Switzerland AG
Date Deposited:	12 Sep 2022 02:40
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460403 Data security and protection @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220405 Cybersecurity @ 100%
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