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FARIMA model-based communication traffic anomaly detection in intelligent electric power substations

Content Provider	IET Digital Library
Author	Yang, Qiang Hao, Weijie Ge, Leijiao Ruan, Wei Chi, Fujian
Abstract	The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyber-attacks. To this end, this study presents a fractional auto-regressive integrated moving average (FARIMA)-based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness.
Starting Page	22
Ending Page	29
Page Count	8
Volume Number	4
e-ISSN	23983396
Issue Number	Issue 1, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cps/4/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5052
Journal	IET Cyber-Physical Systems: Theory & Applications
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-10-19
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Anomaly Detection Solution Collected SCN Data Traffic Computer Communication Cyber Attack Data Security Data Traffic Pattern FARIMA Model-based Communication Traffic Anomaly Detection Intelligent Electric Power Substations Intelligent Electric Substations Operational Performance Power Engineering Computing Protocol SCN Performance SCN Traffic Flow Security of Data Substation Communication Network Substations Telecommunication Traffic Voltage 110.0 KV
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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FARIMA model-based communication traffic anomaly detection in intelligent electric power substations

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