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

Conference paper

Al-Nawasrah, A, Al-Momani, A, Meziane, F. and Alauthman, M 2018. Fast flux botnet detection framework using adaptive dynamic evolving spiking neural network algorithm. 9th International Conference on Information and Communication Systems (ICICS). Irbid, Jordan 03 - 05 Apr 2018 IEEE Xplore. https://doi.org/10.1109/iacs.2018.8355433
AuthorsAl-Nawasrah, A, Al-Momani, A, Meziane, F. and Alauthman, M
TypeConference paper
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.

KeywordsADeSNN; botnet detection; dynamic evolving spiking neural network; Fast-Flux
Year2018
Conference9th International Conference on Information and Communication Systems (ICICS)
Journal2018 9th International Conference on Information and Communication Systems, ICICS 2018
PublisherIEEE Xplore
ISSN2573-3346
Digital Object Identifier (DOI)https://doi.org/10.1109/iacs.2018.8355433
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85048458644&partnerID=MN8TOARS
Publisher's version
File Access Level
Open
Journal citationVol 2018, pp. 7 - 11
ISBN978-153864366-2
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8353113/proceeding
Output statusPublished
Publication dates
Online07 May 2018
03 Apr 2018
Publication process dates
Deposited05 Jun 2023
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