An efficient reinforcement learning-based Botnet detection approach

Mohammad Alauthman, Nauman Aslam, Mouhammd Alkasassbeh, Suleman Khan, Ahmad AL-qerem, Kim-Kwang Raymond Choo

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
58 Downloads (Pure)

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%).
Original languageEnglish
Article number102479
JournalJournal of Network and Computer Applications
Volume150
Early online date2 Nov 2019
DOIs
Publication statusPublished - 15 Jan 2020

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