An efficient reinforcement learning-based Botnet detection approach. (15th January 2020)

• Record Type: Journal Article
• Authors: Alauthman, Mohammad; Aslam, Nauman; Al-kasassbeh, Mouhammd; Khan, Suleman; Al-Qerem, Ahmad; Raymond Choo, Kim-Kwang
• Is Part Of: Journal of network and computer applications. Volume 150(2020)
• Publication Date: 2020-01-15
• Subjects: Botnet detection -- Network security -- Traffic reduction -- Neural network -- C2C -- Reinforcement-learning; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2019.102479
• Languages: English
• ISSNs: 1084-8045

Abstract

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%).