Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

A Novel Learning Algorithm on Probability Measure for Intrusion Detection

Authors
Xiang Fang, Ying Jia
Corresponding Author
Xiang Fang
Available Online August 2016.
DOI
https://doi.org/10.2991/cset-16.2016.31How to use a DOI?
Keywords
Intrusion detection, support vector machine, probability measures, kernal fuction
Abstract
Attacks cyber-based have already seriously threaten the security of network environment and network application with the rapid development and wide application of network services. Intrusion detection plays a vital role in the network security. The machine learning methods have been utilized in Intrusion Detection. Because the network intrusion system has to deal with a huge amount of data, its consumption is too large in the space and time. We present an algorithm that learns from probability measures instead of the specific samples in the traditional support vector machine (SVM).The novel algorithm can increase efficiency by the scale down dataset. The simulation test results on the KDD cup99 dataset show that our method is faster than traditional SVM algorithm at the premise of recognition accuracy.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiang Fang
AU  - Ying Jia
PY  - 2016/08
DA  - 2016/08
TI  - A Novel Learning Algorithm on Probability Measure for Intrusion Detection
PB  - Atlantis Press
SP  - 129
EP  - 132
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.31
DO  - https://doi.org/10.2991/cset-16.2016.31
ID  - Fang2016/08
ER  -