Alauthman, Mohammad, Aslam, Nauman, Alkasassbeh, Mouhammd, Khan, Suleman, AL-qerem, Ahmad and Raymond Choo, Kim-Kwang (2020) An efficient reinforcement learning-based Botnet detection approach. Journal of Network and Computer Applications, 150. p. 102479. ISSN 1084-8045
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Abstract
The use of bot malware and botnets as a tool to facilitate other malicious cyber activities (e.g. distributed denial of service attacks, dissemination of malware and spam, and click fraud). However, detection of botnets, particularly peer-to-peer (P2P) botnets, is challenging. Hence, in this paper we propose a sophisticated traffic reduction mechanism, integrated with a reinforcement learning technique. We then evaluate the proposed approach using real-world network traffic, and achieve a detection rate of 98.3%. The approach also achieves a relatively low false positive rate (i.e. 0.012%).
| Item Type: | Article |
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| Uncontrolled Keywords: | Botnet detection, Network security, Traffic reduction, Neural network, C2C, Reinforcement-learning |
| Subjects: | G400 Computer Science G500 Information Systems |
| Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
| Depositing User: | Elena Carlaw |
| Date Deposited: | 04 Nov 2019 13:55 |
| Last Modified: | 31 Jul 2021 13:18 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/41349 |
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