Proceedings of the 2015 International conference on Applied Science and Engineering Innovation

Approach Research on the Techniques for Network Intrusion Detection Based on Data Mining

Authors
Lina Gong, Tao Xu, Wei Zhang, XuHong Li, Xia Wang, Wenwen Pan
Corresponding Author
Lina Gong
Available Online May 2015.
DOI
10.2991/asei-15.2015.418How to use a DOI?
Keywords
Network Anomaly; Network Intrusion Detection; Data Mining;
Abstract

Along with computer technology's popularization and application and popularization, the network technology has been widely used, the resulting network security issues have become increasingly prominent, the network itself and network information system which is based on the potential security risks. Expounds the concepts of intrusion detection and data mining, common intrusion detection techniques and models, and analyzes the data mining technology in intrusion detection system application and optimization provide reference and perfect network intrusion detection system and reference.

Copyright
© 2015, 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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Volume Title
Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
Series
Advances in Engineering Research
Publication Date
May 2015
ISBN
978-94-62520-94-3
ISSN
2352-5401
DOI
10.2991/asei-15.2015.418How to use a DOI?
Copyright
© 2015, 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/).

Cite this article

TY  - CONF
AU  - Lina Gong
AU  - Tao Xu
AU  - Wei Zhang
AU  - XuHong Li
AU  - Xia Wang
AU  - Wenwen Pan
PY  - 2015/05
DA  - 2015/05
TI  - Approach Research on the Techniques for Network Intrusion Detection Based on Data Mining
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 2133
EP  - 2136
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.418
DO  - 10.2991/asei-15.2015.418
ID  - Gong2015/05
ER  -