Research on equipment identification technology of power monitoring system

Hongjie Zhang; Qi Wang; Tiejun Zheng; Jianwei He; Shaohua Yong

doi:10.1117/12.2611059

14 October 2021 Research on equipment identification technology of power monitoring system

Hongjie Zhang, Qi Wang, Tiejun Zheng, Jianwei He, Shaohua Yong

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119302X (2021) https://doi.org/10.1117/12.2611059
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

With the rapid development of network technology, a large number of diversified network equipment are applied to the power monitoring system, which not only greatly improves the production efficiency of the power system, but also challenges its own security management. Accurate and comprehensive identification of network equipment is the premise of effective management of network equipment, and also the basis of security threat analysis. In this paper, an equipment identification method based on traffic analysis is proposed. By passively monitoring the traffic in the network, the representative features of the data flow are extracted from multiple dimensions and analyzed. The C4.5 decision tree algorithm is used to generate the identification model, and the equipment in the network is identified by this model, The accuracy rate of equipment identification can reach 94%.

Citation Download Citation

Hongjie Zhang, Qi Wang, Tiejun Zheng, Jianwei He, and Shaohua Yong "Research on equipment identification technology of power monitoring system", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119302X (14 October 2021); https://doi.org/10.1117/12.2611059

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available