Title: The effectiveness of classification algorithms on IPv6 IID construction

Authors: Clinton Carpene; Michael N. Johnstone; Andrew J. Woodward

Addresses: School of Computer and Security Science, ECU Security Research Institute, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia ' School of Computer and Security Science, ECU Security Research Institute, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia ' School of Computer and Security Science, ECU Security Research Institute, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia

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: privacy; machine learning; IPv6 interface identifier; IID construction; artificial neural networks; ANNs; naive Bayesian classifiers; NBC; classification algorithms; general-purpose computing; graphics processing units; GPUs; IID addresses.

DOI: 10.1504/IJAACS.2017.082735

International Journal of Autonomous and Adaptive Communications Systems, 2017 Vol.10 No.1, pp.15 - 22

Received: 13 Sep 2013
Accepted: 12 Jan 2014

Published online: 10 Mar 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article