Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

The Research about Data Mining of Network Intrusion Based on Apriori Algorithm

Authors
Jigang Zheng, Jingmei Zhang
Corresponding Author
Jigang Zheng
Available Online February 2017.
DOI
10.2991/emcm-16.2017.128How to use a DOI?
Keywords
Apriori algorithm; Data mining; Network intrusion; Association rules
Abstract

Found that the association between each characteristic attributes and behavior based on network intrusion data to achieve intrusion valuable data extraction and analysis,the use of denial of service attacks recorded KDDCup99 dataset simulation experiments.Weka software using association rule mining algorithms for different types of denial of service attack attribute characteristics for analysis,has been contact between the different characteristics of different attributes classification identifies the types of attacks,to improve the efficiency and accuracy of intrusion detection has an excellent role,has a certain value.

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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Volume Title
Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
Series
Advances in Computer Science Research
Publication Date
February 2017
ISBN
10.2991/emcm-16.2017.128
ISSN
2352-538X
DOI
10.2991/emcm-16.2017.128How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Jigang Zheng
AU  - Jingmei Zhang
PY  - 2017/02
DA  - 2017/02
TI  - The Research about Data Mining of Network Intrusion Based on Apriori Algorithm
BT  - Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcm-16.2017.128
DO  - 10.2991/emcm-16.2017.128
ID  - Zheng2017/02
ER  -