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ATMSim: a Hadoop and self-similarity-based simulator for collecting, detecting, measuring and analysing anomalous traffic

ATMSim: a Hadoop and self-similarity-based simulator for collecting, detecting, measuring and... Recent developments in information and communication networks as well as the popularity of smartphones have been contributing to a geometrical increase in internet traffic. In relation to this, this study aims to collect, detect, measure and analyse the DDoS attacks typical of increasing security incidents on internet and network attacks. To this end, a large volume of normal traffic, coming in through an internal LAN of a university, and anomalous traffic including DDoS attacks using an ATMSim analysis package operating on the basis of network flow information, was generated. The self-similarity estimation techniques were used to analyse the behaviour of the collected and generated normal and anomalous traffic. This information was then used to prove graphically and quantitatively that the analysis reveals a great difference between the normal traffic and the anomalous traffic in terms of self-similarity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web and Grid Services Inderscience Publishers

ATMSim: a Hadoop and self-similarity-based simulator for collecting, detecting, measuring and analysing anomalous traffic

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1741-1106
eISSN
1741-1114
DOI
10.1504/IJWGS.2017.085148
Publisher site
See Article on Publisher Site

Abstract

Recent developments in information and communication networks as well as the popularity of smartphones have been contributing to a geometrical increase in internet traffic. In relation to this, this study aims to collect, detect, measure and analyse the DDoS attacks typical of increasing security incidents on internet and network attacks. To this end, a large volume of normal traffic, coming in through an internal LAN of a university, and anomalous traffic including DDoS attacks using an ATMSim analysis package operating on the basis of network flow information, was generated. The self-similarity estimation techniques were used to analyse the behaviour of the collected and generated normal and anomalous traffic. This information was then used to prove graphically and quantitatively that the analysis reveals a great difference between the normal traffic and the anomalous traffic in terms of self-similarity.

Journal

International Journal of Web and Grid ServicesInderscience Publishers

Published: Jan 1, 2017

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