Research and Application of Device Fingerprint

DOI

10.2991/mecae-17.2017.87How to use a DOI?

Keywords

Access detection of video dedicated network, Device fingerprint, Decision tree, Information entropy

Abstract

Because many acquisition equipments of video network are deployed in public areas, they face many security issues. It is very important to connect the video equipments to the video dedicated network safely and efficiently. In this paper, we propose a decision tree classification algorithm of device fingerprint to solve the problem. According to the characteristics of video equipments, we design the device fingerprint on the basis of the operating system fingerprint. Meanwhile, we also propose the collection and storage methods of device fingerprint. The decision tree classification algorithm of device equipment can detect the untrusted devices in the video dedicated network, and can also prompt the system to send alarm information. In a word, this project can effectively prevent the illegal intrusion and keep the data in video dedicated network from leaking.

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  - Xinming Yin
AU  - Zhengliang Hu
AU  - Guoliang Chen
AU  - Haiye Huang
AU  - Zhiwei Cao
PY  - 2017/03
DA  - 2017/03
TI  - Research and Application of Device Fingerprint
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 457
EP  - 461
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.87
DO  - 10.2991/mecae-17.2017.87
ID  - Yin2017/03
ER  -