Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering

Web robot detection with semi-supervised learning method

Authors
Dong Wang, Lei Xi, Hui Zhang, Hebing Liu, Hao Zhang, Ting Song
Corresponding Author
Dong Wang
Available Online August 2015.
DOI
10.2991/ic3me-15.2015.407How to use a DOI?
Keywords
Web robot detection, Semi-supervised learning, Support Vector Machine
Abstract

Web robot is an automated information gathering program that has brought a lot of problems such as information leakage, resource occupation and network security threaten. It is necessary to effectively detect and control the web access comes from web robots. Summarizes the existing categories of web robot detection method, we propose a new detection method with semi-supervised support vector machine. The experiments based on the same test data set are presented to show that the new method is superior to other robot detection methods.

Copyright
© 2015, 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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Volume Title
Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
August 2015
ISBN
978-94-6252-100-1
ISSN
2352-5401
DOI
10.2991/ic3me-15.2015.407How to use a DOI?
Copyright
© 2015, 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/).

Cite this article

TY  - CONF
AU  - Dong Wang
AU  - Lei Xi
AU  - Hui Zhang
AU  - Hebing Liu
AU  - Hao Zhang
AU  - Ting Song
PY  - 2015/08
DA  - 2015/08
TI  - Web robot detection with semi-supervised learning method
BT  - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
PB  - Atlantis Press
SP  - 2123
EP  - 2128
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.407
DO  - 10.2991/ic3me-15.2015.407
ID  - Wang2015/08
ER  -