Extracting salient features for network intrusion detection using machine learning methods
DOI:
https://doi.org/10.18489/sacj.v52i0.200Keywords:
network intrusion detection, feature selection, machine learning, decision treesAbstract
This work presents a data preprocessing and feature selection framework to support data mining and network security experts in minimal feature set selection of intrusion detection data. This process is supported by detailed visualisation and examination of class distributions. Distribution histograms, scatter plots and information gain are presented as supportive feature reduction tools. The feature reduction process applied is based on decision tree pruning and backward elimination. This paper starts with an analysis of the KDD Cup '99 datasets and their potential for feature reduction. The dataset consists of connection records with 41 features whose relevance for intrusion detection are not clear. All traffic is either classified `normal' or into the four attack types denial-of-service, network probe, remote-to-local or user-to-root. Using our custom feature selection process, we show how we can significantly reduce the number features in the dataset to a few salient features. We conclude by presenting minimal sets with 4--8 salient features for two-class and multi-class categorisation for detecting intrusions, as well as for the detection of individual attack classes; the performance using a static classifier compares favourably to the performance using all features available. The suggested process is of general nature and can be applied to any similar dataset.Downloads
Published
2014-06-29
Issue
Section
Research Papers (general)
License
Copyright of all work published here subsists in the authors. While SACJ retains right of first publication, subsequent re-publication is expressly permitted provided the original SACJ publication is acknowledged and cited, according to the terms detailed below. If plagiarism is detected during review, a paper may be summarily rejected and will not be accepted unless even minor infringements are corrected. Should plagiarism be detected after a paper is published, the Editor reserves the right to withdraw a paper from publication. We expect authors to be honest in representing work as their own, and to respect the time and effort our reviewers put in without an undue burden of policing plagiarism, and hence take violations seriously. SACJ applies the Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0) to all papers published in this journal. Authors who publish with SACJ agree to the following:- Authors retain copyright and grant SACJ right of first publication. The work is additionally licensed under a Creative Commons Attribution Non-Commercial License that requires others who share the work to acknowledge the work’s authorship and initial publication in SACJ. Should anyone else wish to make commercial use of the work, SACJ cedes the right to the author to negotiate terms and does not expect to be paid any royalties.
- Authors may enter into additional arrangements for non-exclusive distribution of the SACJ-published version of the work (e.g., post it to a repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are required to refrain from posting their work online prior to completion of reviews so as not to compromise double-blind reviewing or confuse plagiarism checks.