Collaborative approach to network behaviour analysis based on hardware-accelerated FlowMon probes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F09%3A00035628" target="_blank" >RIV/00216224:14330/09:00035628 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/63839172:_____/09:00001149

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Collaborative approach to network behaviour analysis based on hardware-accelerated FlowMon probes

Popis výsledku v původním jazyce

Network behaviour analysis techniques are designed to detect intrusions and other undesirable behaviour in computer networks by analysing the traffic statistics. We present an efficient framework for integration of anomaly detection algorithms working onthe identical input data. This framework is based on high-speed network traffic acquisition subsystem and on trust modelling, a well-established set of techniques from the multi-agent system field. Trust-based integration of algorithms results in classification with lower error rate, especially in terms of false positives. The presented system is suitable for both online and offline processing, and introduces a relatively low computational overhead compared to deployment of isolated anomaly detection algorithms.

Název v anglickém jazyce

Collaborative approach to network behaviour analysis based on hardware-accelerated FlowMon probes

Popis výsledku anglicky

Network behaviour analysis techniques are designed to detect intrusions and other undesirable behaviour in computer networks by analysing the traffic statistics. We present an efficient framework for integration of anomaly detection algorithms working onthe identical input data. This framework is based on high-speed network traffic acquisition subsystem and on trust modelling, a well-established set of techniques from the multi-agent system field. Trust-based integration of algorithms results in classification with lower error rate, especially in terms of false positives. The presented system is suitable for both online and offline processing, and introduces a relatively low computational overhead compared to deployment of isolated anomaly detection algorithms.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Electronic Security and Digital Forensics

ISSN

1751-911X

e-ISSN

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Svazek periodika

2

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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