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

Journal article

Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305
AuthorsAlmomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid
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.

KeywordsHardware and Architecture; dynamic evolving spiking neural network; fast-flux techniques detection; network security; botnet
Year2021
JournalInternational Journal of Ad Hoc and Ubiquitous Computing
Journal citation36 (1), p. 50
PublisherInderscience
ISSN1743-8225
1743-8233
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cosrev.2020.100305
Web address (URL)http://hdl.handle.net/10545/625719
hdl:10545/625719
Publication dates28 Jan 2021
Publication process dates
Deposited16 Apr 2021, 10:05
Accepted2020
ContributorsAl-Balqa Applied University, Irbid, Jordan, Taibah University, Median, Saudia Arabia, Zarqa University, Jordan and University of Derby
File
File Access Level
Open
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