Referenced Items are not available for this document.
The paper investigates the use of artificial neural networks (ANN) to unconventionally classify Internet traffic. Structurally and functionally, the classifier used is a feedforward multilayer layer perceptron (FFMLP) network trained using backpropagation. The inputs are random samples of bits from a bit stream (i.e. all the inputs are either 1 or 0). The data was collected and pre-processed, then used to train, test and evaluate the classifier. Despite the lower capability to identify certain data types, the algorithm has shown that it has very good features as a classifier. SMTP, TELNET, FTP, HTTP, IP TELEPHONY and UDP data types were used in the investigation
Published in:
Electrical and Computer Engineering, 2001. Canadian Conference on
(Volume:1
)
Date of Conference: 2001
- Page(s):
- 667 - 672 vol.1
- Meeting Date :
- 13 May 2001-16 May 2001
- ISSN :
- 0840-7789
- Print ISBN:
- 0-7803-6715-4
- INSPEC Accession Number:
- 7068783
- Conference Location :
- Toronto, Ont.
- Digital Object Identifier :
- 10.1109/CCECE.2001.933764
- Product Type:
- Conference Publications
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