Intrusion detection method based on cloud model and semi-supervised clustering dynamic weighting

DOI

10.2991/meita-16.2017.1How to use a DOI?

Keywords

Intrusion detection; semi - supervised clustering; active learning; intrusion prevention

Abstract

Aiming at the problem of low detection rate and high false positive rate of intrusion detection system, a cloud model semi-supervised clustering dynamic weighting intrusion detection method is put forward. As the attribute contributes to the classification difference, the cloud was near relative degree. The method of calculating attribute weight is given. With the semi-supervised clustering algorithm as the basis, the cloud model is constructed and the cloud classifier is constructed. The dynamic weights of attributes are used to classify the cloud classifier by updating the cloud model. Finally, the simulation results show that the proposed method has better detection performance and improves the performance of intrusion detection system.

Copyright

© 2017, 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  - Liping Wang
PY  - 2017/02
DA  - 2017/02
TI  - Intrusion detection method based on cloud model and semi-supervised clustering dynamic weighting
BT  - Proceedings of the 2016 2nd International Conference on Materials Engineering and Information Technology Applications (MEITA 2016)
PB  - Atlantis Press
SP  - 1
EP  - 5
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-16.2017.1
DO  - 10.2991/meita-16.2017.1
ID  - Wang2017/02
ER  -