Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming
In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a...
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| Format: | Online |
| Language: | English |
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Hindawi Publishing Corporation
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4083271/ |
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pubmed-40832712014-07-16 Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming Tseng, Kuo-Kun Ji, Yuzhu Liu, Yiming Huang, Nai-Lun Zeng, Fufu Lin, Fang-Ying Research Article In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a signal waveform and compute suspicious characteristics of the waveform. Network streaming according to these selected waveform features by our newly designed Aho-Corasick (AC) automatons can be classified. We developed two versions, that is, basic AC and advanced AC-histogram waveform automata, and conducted comprehensive experimentation. The results confirm that our approach is feasible and suitable for deployment. Hindawi Publishing Corporation 2014 2014-06-16 /pmc/articles/PMC4083271/ /pubmed/25032231 http://dx.doi.org/10.1155/2014/647216 Text en Copyright © 2014 Kuo-Kun Tseng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Tseng, Kuo-Kun Ji, Yuzhu Liu, Yiming Huang, Nai-Lun Zeng, Fufu Lin, Fang-Ying |
| spellingShingle |
Tseng, Kuo-Kun Ji, Yuzhu Liu, Yiming Huang, Nai-Lun Zeng, Fufu Lin, Fang-Ying Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| author_facet |
Tseng, Kuo-Kun Ji, Yuzhu Liu, Yiming Huang, Nai-Lun Zeng, Fufu Lin, Fang-Ying |
| author_sort |
Tseng, Kuo-Kun |
| title |
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| title_short |
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| title_full |
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| title_fullStr |
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| title_full_unstemmed |
Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming |
| title_sort |
signal waveform detection with statistical automaton for internet and web service streaming |
| description |
In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a signal waveform and compute suspicious characteristics of the waveform. Network streaming according to these selected waveform features by our newly designed Aho-Corasick (AC) automatons can be classified. We developed two versions, that is, basic AC and advanced AC-histogram waveform automata, and conducted comprehensive experimentation. The results confirm that our approach is feasible and suitable for deployment. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4083271/ |
| _version_ |
1613108674523299840 |