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The effectiveness of classification algorithms on IPv6 IID construction

The effectiveness of classification algorithms on IPv6 IID construction This study assessed the effectiveness of classifying IPv6 interface identifier (IID) address construction using machine learning algorithms. It was observed that IID construction can be reliably determined through the usage of assisted machine learning algorithms such as the naïve Bayesian classifiers (NBC) or artificial neural networks (ANNs). It was also observed that the NBC classification, whilst more efficient, was less accurate than the use of ANN for classifying interface identifiers. Training times for an unoptimised ANN were seen to be far greater than NBC, which may be a considerable limitation to its effectiveness in real world applications (such as log or traffic analysis). Future research will continue to improve the classification training times for ANN situations, potentially involving general-purpose computing on graphics processing units (GPGPU) systems, as well as applying the techniques to real world applications such as IPv6 IDS sensors, honeypots or honeynets. Keywords: IPv6; privacy; machine learning; IID construction; artificial neural networks; ANNs; naïve Bayesian classifiers; NBC. Reference to this paper should be made as follows: Carpene, C., Johnstone, M.N. and Woodward, A.J. (2017) `The effectiveness of classification algorithms on IPv6 IID construction', Int. J. Autonomous and Adaptive Communications Systems, Vol. 10, No. 1, pp.15­22. Biographical http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Autonomous and Adaptive Communications Systems Inderscience Publishers

The effectiveness of classification algorithms on IPv6 IID construction

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References (8)

Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1754-8632
eISSN
1754-8640
DOI
10.1504/IJAACS.2017.082735
Publisher site
See Article on Publisher Site

Abstract

This study assessed the effectiveness of classifying IPv6 interface identifier (IID) address construction using machine learning algorithms. It was observed that IID construction can be reliably determined through the usage of assisted machine learning algorithms such as the naïve Bayesian classifiers (NBC) or artificial neural networks (ANNs). It was also observed that the NBC classification, whilst more efficient, was less accurate than the use of ANN for classifying interface identifiers. Training times for an unoptimised ANN were seen to be far greater than NBC, which may be a considerable limitation to its effectiveness in real world applications (such as log or traffic analysis). Future research will continue to improve the classification training times for ANN situations, potentially involving general-purpose computing on graphics processing units (GPGPU) systems, as well as applying the techniques to real world applications such as IPv6 IDS sensors, honeypots or honeynets. Keywords: IPv6; privacy; machine learning; IID construction; artificial neural networks; ANNs; naïve Bayesian classifiers; NBC. Reference to this paper should be made as follows: Carpene, C., Johnstone, M.N. and Woodward, A.J. (2017) `The effectiveness of classification algorithms on IPv6 IID construction', Int. J. Autonomous and Adaptive Communications Systems, Vol. 10, No. 1, pp.15­22. Biographical

Journal

International Journal of Autonomous and Adaptive Communications Systems – Inderscience Publishers

Published: Jan 1, 2017

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