Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758221
Title: Fast flux botnet detection based on adaptive dynamic evolving spiking neural network
Author: Al Nawasrah, A.
ISNI:       0000 0004 7430 9967
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
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 (C&C) servers are the backbone of botnet communications, where the bots and botmaster send reports and attack orders to each other, respectively. Botnets are also categorised according to their C&C protocols. A Domain Name System (DNS) method known as Fast-Flux Service Network (FFSN) is a special type of botnet that 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 domains, nevertheless they have low detection accuracy especially with zero-day domain, quite a long detection time, and consume high memory storage. In this research we propose a new system called Fast Flux Killer System (FFKA) that has the ability to detect “zero-day” FF-Domains in online mode with an implementation constructed on Adaptive Dynamic evolving Spiking Neural Network (ADeSNN) and in an offline mode to enhance the classification process which is a novelty in this field. The adaptation includes the initial weight, testing criteria, parameters customization, and parameters adjustment. The proposed system is expected to detect fast flux domains in online mode with high detection accuracy and low false positive and false negative rates respectively. It is also expected to have a high level of performance and the proposed system is designed to work for a lifetime with low memory usage. Three public datasets are exploited in the experiments to show the effects of the adaptive ADeSNN algorithm, two of them conducted on the ADeSNN algorithm itself and the last one on the process of detecting fast flux domains. The experiments showed an improved accuracy when using the proposed adaptive ADeSNN over the original algorithm. It also achieved a high detection accuracy in detecting zero-day fast flux domains that was about (99.54%) in an online mode, when using the public fast flux dataset. Finally, the improvements made to the performance of the adaptive algorithm are confirmed by the experiments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.758221  DOI: Not available
Share: