The University of Southampton
University of Southampton Institutional Repository

Machine Learning for Intrusion Detection: Modeling the Distribution Shift

Farran, Bassam, Saunders, Craig and Niranjan, Mahesan (2010) Machine Learning for Intrusion Detection: Modeling the Distribution Shift At IEEE Workshop on Machine Learning for Signal Processing, Finland. 29 Aug - 01 Sep 2010.

Record type: Conference or Workshop Item (Paper)


This paper addresses two important issue that arise in formulating and solving computer intrusion detection as a machine learning problem, a topic that has attracted considerable attention in recent years including a community wide competition using a common data set known as the KDD Cup ’99. The first of these problems we address is the size of the data set, 5 × 10^6 by 41 features, which makes conventional learning algorithms impractical. In previous work, we introduced a one-pass non-parametric classification technique called Voted Spheres, which carves up the input space into a series of overlapping hyperspheres. Training data seen within each hypersphere is used in a voting scheme during testing on unseen data. Secondly, we address the problem of distribution shift whereby the training and test data may be drawn from slightly different probability densities, while the conditional densities of class membership for a given datum remains the same. We adopt two recent techniques from the literature, density weighting and kernel mean matching, to enhance the Voted Spheres technique to deal with such distribution disparities. We demonstrate that substantial performance gains can be achieved using these techniques on the KDD cup data set.

PDF FarranMLSP2010.pdf - Other
Download (224kB)

More information

Published date: August 2010
Additional Information: Event Dates: August 29 - September 1, 2010
Venue - Dates: IEEE Workshop on Machine Learning for Signal Processing, Finland, 2010-08-29 - 2010-09-01
Organisations: Southampton Wireless Group


Local EPrints ID: 272869
PURE UUID: bd89b782-ae79-4fe5-9cc9-41976416b670

Catalogue record

Date deposited: 28 Sep 2011 09:18
Last modified: 18 Jul 2017 06:19

Export record


Author: Bassam Farran
Author: Craig Saunders

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.