Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

Concept Drift Based on Subspace Learning for Intrusion Detection

Authors
Bin Wu, Hai-Zhuo Lin, Lin Feng
Corresponding Author
Bin Wu
Available Online November 2016.
DOI
https://doi.org/10.2991/ceis-16.2016.85How to use a DOI?
Keywords
intrusion detection; concept drift; subspace learning
Abstract
In recent years, Intrusion Detection System(IDS) thrives and becomes the main approach for detecting and defending internet attack. And network streams are the best data sources for studying network attack. In order to detect intrusions, concept drifting method is applied. What is more, the subspace learning based concept drifting method is fit for dealing with high dimensional data streams. It can not only detect the concept drift, but also reduce the dimensionality at the same time, which makes the detection more efficient. We also design model for judging concept drift, which checks the deviation of the error term of projection variance and the deviation of the error term of projection cosine. The experiment of KDD data set validates that our method is more efficient and accurate.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering and Information Systems
Part of series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-283-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/ceis-16.2016.85How 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  - Bin Wu
AU  - Hai-Zhuo Lin
AU  - Lin Feng
PY  - 2016/11
DA  - 2016/11
TI  - Concept Drift Based on Subspace Learning for Intrusion Detection
BT  - 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/ceis-16.2016.85
DO  - https://doi.org/10.2991/ceis-16.2016.85
ID  - Wu2016/11
ER  -