AppIdent - Tool for Network Application Protocols Identification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APR29229" target="_blank" >RIV/00216305:26230/17:PR29229 - isvavai.cz</a>
Result on the web
<a href="https://pluskal.github.io/AppIdent/" target="_blank" >https://pluskal.github.io/AppIdent/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
AppIdent - Tool for Network Application Protocols Identification
Original language description
Network traffic classification is an absolute necessity for network monitoring, security analysis, and digital forensics. Without accurate traffic classification, computation demands on analysis of all IP flows are enormous. Classification can also reduce the number of flows that need to be analyzed, prioritize, and order them for an investigator to analyze the most forensically significant first. This paper presents an automatic feature elimination method based on a feature correlation matrix. Furthermore, we compare two algorithms adapted from literature, that offer high accuracy and acceptable performance, and our algorithm -- Enhanced Statistical Protocol Identification (ESPI). Each of these algorithms is used with a subset of features that best suits it. We evaluate these algorithms on their ability to identify application layer protocols and additionally applications themselves. Experiments show that the Random Forest based classifier yields the most promising results, whereas our algorithm provides an interesting tradeoff between higher performance and slightly lower accuracy.
Czech name
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Czech description
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Classification
Type
R - Software
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
Result continuities
Project
<a href="/en/project/VI20172020062" target="_blank" >VI20172020062: Integrated platform for analysis of digital data from security incidents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Internal product ID
AppIdent
Technical parameters
Pro informace o licenčních podmínkách prosím kontaktujte: Mgr. Michaela Burianová, Výzkumné centrum informačních technologií, Fakulta informačních technologií VUT v Brně, Božetěchova 2, 612 66 Brno, 541 141 470.
Economical parameters
Pro informace o licenčních podmínkách prosím kontaktujte: Mgr. Michaela Burianová, Výzkumné centrum informačních technologií, Fakulta informačních technologií VUT v Brně, Božetěchova 2, 612 66 Brno, 541 141 470.
Owner IČO
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Owner name
Fakulta informačních technologií