An Intrusion Detection System Based on Big Data for Power System

DOI

10.2991/isaeece-16.2016.62How to use a DOI?

Keywords

Power System, CPS, Data Mining, Intrusion Detection

Abstract

On the background of information and energy interconnection, the whole power system generated a huge amount of data with diverse structure, complicated sources and large scale from both cyber devices and physical components, which is a typical cyber-physical system (CPS). These data exhibit data feature such as large quantity, complicated data item, complex processing logic, long storage cycle and high frequency calculation. Therefore, from a CPS perspective, the power system is facing intrusions that are more damaging, complicated and wide spreading. Currently, most power system network intrusion detection systems are founded manually. Especially, the detection knowledge used for identify intrusion action is provided by security expert and complied into the network intrusion detection system(IDS). The defect of this approach is that it needs the continuing input of upgraded knowledge concerning the intrusion detection, which may not suit for the complex power CPS. Therefore, the expansion and adaptability of such term is not suitable in the context of big data problem. In this paper, we propose hierarchic IDS that combines misuse detection and abnormal detection for Power System. Data mining algorithms are used to build the rules by studying and analyzing historical monitor date. The prototype implemented proves that the model proposed can detect cyber-attacks accurately with low false positive and false negative rate.

Copyright

© 2016, 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  - Sicheng Zeng
PY  - 2016/04
DA  - 2016/04
TI  - An Intrusion Detection System Based on Big Data for Power System
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 322
EP  - 328
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.62
DO  - 10.2991/isaeece-16.2016.62
ID  - Zeng2016/04
ER  -