A Semi-Supervised IDS Alert Classification Model Based on Alert Context

DOI

10.2991/iccsee.2013.187How to use a DOI?

Keywords

alert classification model, semi-supervised learning, alert context

Abstract

How to filtering false positives is a fundamental problem of IDS. Constructing alert classification model is one of efficient methods. However, the high cost of preparing training data and classification feature selection are key points in the problem. This paper gives a semi-supervised alert classification model which makes use of the power of semi-supervised learning. Moreover, four classification features about alert context are introduced to improve classification accuracy. Experiments conducted on the DARPA 1999 dataset show that the use of the alert context properties can increase the classification accuracy by about 3 percent.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haibin Mei
AU  - Minghua Zhang
PY  - 2013/03
DA  - 2013/03
TI  - A Semi-Supervised IDS Alert Classification Model Based on Alert Context
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 738
EP  - 741
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.187
DO  - 10.2991/iccsee.2013.187
ID  - Mei2013/03
ER  -