Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

The Application of High Dimensional Data Mining Based on Big Data to Intrusion Detection

Authors
Jinhua Liu
Corresponding Author
Jinhua Liu
Available Online March 2017.
DOI
10.2991/msam-17.2017.38How to use a DOI?
Keywords
high dimensional data mining; big data; intrusion detection
Abstract

Under the historical background of big data, the traditional data processing methods can no longer meet the requirement proposed by the intrusion detection of systems. With the current development of information technology, the data in systems become increasingly complex, and the workload of data mining also becomes increasingly large in the intrusion detection of systems, which invisibly increases the difficulty in intrusion detection. In this paper, issues related to the application of high dimensional data mining technology based on big data to intrusion detection are analyzed.

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 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
10.2991/msam-17.2017.38
ISSN
1951-6851
DOI
10.2991/msam-17.2017.38How 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  - Jinhua Liu
PY  - 2017/03
DA  - 2017/03
TI  - The Application of High Dimensional Data Mining Based on Big Data to Intrusion Detection
BT  - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 169
EP  - 171
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.38
DO  - 10.2991/msam-17.2017.38
ID  - Liu2017/03
ER  -