Research on Electric Power Information Systems Network Security Situation Awareness Based on Big Data Technology

DOI

10.2991/eeeis-17.2017.80How to use a DOI?

Keywords

big data, network security, situation awareness, situation prediction, TSA.

Abstract

With the rapid development of the network scale and its applications, network security threats continue to increase, a single network security protection technology could not meet the requirement. Network security situation awareness can dynamically reflect the overall network security and predict network security development trends. Big data analytics technology provides the basis for the research of network security situation awareness. In this paper, we explore the problem of network security situation awareness for electric power information systems under big data environment. In order to monitor network security problems, a network security situation awareness technology based on multi-source logging methods by utilizing big data analysis is proposed. We apply this technique to the information system environment of a certain electric power company. We deployed network traffic security analyzer (TSA) in the export of company Internet network. It can acquire and storage the original network traffic in real time. By using the big data visualization analysis tool and rich data display component, the realization of the multidimensional graphical visualization of the analysis results is presented.

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  - Dong-Lan LIU
AU  - Dong LI
AU  - Lei MA
AU  - Xin LIU
AU  - Hao YU
AU  - Ying-Xian CHANG
AU  - Jian-Fei CHEN
PY  - 2017/09
DA  - 2017/09
TI  - Research on Electric Power Information Systems Network Security Situation Awareness Based on Big Data Technology
BT  - Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017)
PB  - Atlantis Press
SP  - 540
EP  - 547
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-17.2017.80
DO  - 10.2991/eeeis-17.2017.80
ID  - LIU2017/09
ER  -