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A self-learning system for identifying harmful network information

Shi, Wei, Zhou, Wanlei and Zheng, W. 2004, A self-learning system for identifying harmful network information, in iiWAS 2004 : Sixth International Conference on Information Integration and Web-based Applications Services, 27-29 September 2004, Jakarta, Indonesia, Austrian Computer Society, Vienna, Austria, pp. 621-630.

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Title A self-learning system for identifying harmful network information
Author(s) Shi, Wei
Zhou, Wanlei
Zheng, W.
Conference name Information Integrationand Web-based Applications Services (6th : 2004, Jakarta)
Conference location Jakarta, Indonesia
Conference dates September 27-29 2004
Title of proceedings iiWAS 2004 : Sixth International Conference on Information Integration and Web-based Applications Services, 27-29 September 2004, Jakarta, Indonesia
Editor(s) Kotsis, G.
Bressan, S.
Taniar, D.
Ibrahim, I. K.
Publication date 2004
Start page 621
End page 630
Publisher Austrian Computer Society
Place of publication Vienna, Austria
Summary Network information identification is a “hot” topic currently. This paper designs a self-learning system using neural network algorithm for identifying the harmful network messages of both Chinese and English languages. The system segments the message into words and creates key word vector which characterizes the harmful network information. The BP algorithm is taken advantage of to train the neural network. The result of training and studying of the neural network can be applied onto many network applications based on message identification. The result of experiments demonstrates that our system has a high degree of accuracy.
Notes Reproduced with the specific permission of the copyright owner.
ISBN 3902134720
9783902134721
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2004, Austrian Computer Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005544

Document type: Conference Paper
Collections: School of Information Technology
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