Title: Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

Authors: Ammar Almomani; Ahmad Al-Nawasrah; Mohammad Alauthman; Mohammed Azmi Al-Betar; Farid Meziane

Addresses: IT Department, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan ' Computer Science and Information Department, Taibah University, Medina, Saudi Arabia ' Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan ' IT Department, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates ' Data Science Research Centre, University of Derby, UK

Abstract: A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. In the present research, we aimed to propose a new system. This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy, low false positive and negative rates, respectively. It shows a high performance. The algorithm's proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.

Keywords: dynamic evolving spiking neural network; fast-flux techniques detection; network security; botnet.

DOI: 10.1504/IJAHUC.2021.112981

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.36 No.1, pp.50 - 65

Received: 24 Mar 2020
Accepted: 03 Aug 2020
Published online: 12 Feb 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article