Fast flux botnet detection framework using adaptive dynamic evolving spiking neural network algorithm

Al-Nawasrah, A, Al-Momani, A, Meziane, F ORCID: 0000-0001-9811-6914 and Alauthman, M 2018, Fast flux botnet detection framework using adaptive dynamic evolving spiking neural network algorithm , in: The 9th International Conference on Information and Communication Systems (ICICS 2018), 3-5 April 2018, Irbid, Jordan.

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Abstract

A botnet, a set of compromised machines controlled distantly by an attacker, is the basis of numerous security threats around the world. Command and Control servers are the backbones of botnet communications, where the bots and botmasters send report and attack orders to each other. Botnets are also categorized according to their C&C protocols. A Domain Name System method known as Fast-Flux Service Network (FFSN) – a special type of botnet – has been engaged by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain name over time. Although several methods have been suggested for detecting FFSNs, they have low detection accuracy especially with zero-day domain. In this research, we propose a new system called Fast Flux Killer System (FFKS) that has the ability to detect FF-Domains in online mode with an implementation constructed on Adaptive Dynamic evolving Spiking Neural Network (ADeSNN). The proposed system proved its ability to detect FF domains in online mode with high detection accuracy (98.77%) compare with other algorithms, with low false positive and negative rates respectively. It is also proved a high level of performance. Additionally, the proposed adaptation of the algorithm enhanced and helped in the parameters customization process.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Proceedings, the 9th International Conference on Information and Communication Systems (ICICS 2018)
Publisher: IEEE
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 10 Apr 2018 12:50
Last Modified: 10 Apr 2018 14:07
URI: http://usir.salford.ac.uk/id/eprint/46622

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