An efficient reinforcement learning-based Botnet detection approach

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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Official URL: https://doi.org/10.1016/j.jnca.2019.102479

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
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: 22 Nov 2019 15:30
URI: http://nrl.northumbria.ac.uk/id/eprint/41349

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