Adaptive Multi-Agent System for Network Traffic Monitoring

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00158346" target="_blank" >RIV/68407700:21230/09:00158346 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/63839172:_____/09:00006680 RIV/00216224:14610/09:00042538

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Multi-Agent System for Network Traffic Monitoring

Popis výsledku v původním jazyce

We present an application of agent-based data mining for a near-real time detection of attacks against the computer networks and connected hosts. The presented system processes the statistics of network traffic provided by high-speed network monitoring cards and uses a set of known anomaly detection techniques to identify the anomalous behavior. The individual anomaly detection methods have relatively high error rates that make them unfit for most practical deployments. Based on the agent-based trust modeling technique, our system fuses the data provided by snímaly detection methods and progressively builds a better classification, with an acceptable error rate. The system uses agent-based self-adaptation techniques to dynamically align its structure with the changes in network traffic structure and attacks.

Název v anglickém jazyce

Adaptive Multi-Agent System for Network Traffic Monitoring

Popis výsledku anglicky

We present an application of agent-based data mining for a near-real time detection of attacks against the computer networks and connected hosts. The presented system processes the statistics of network traffic provided by high-speed network monitoring cards and uses a set of known anomaly detection techniques to identify the anomalous behavior. The individual anomaly detection methods have relatively high error rates that make them unfit for most practical deployments. Based on the agent-based trust modeling technique, our system fuses the data provided by snímaly detection methods and progressively builds a better classification, with an acceptable error rate. The system uses agent-based self-adaptation techniques to dynamically align its structure with the changes in network traffic structure and attacks.

Druh

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

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

IEEE Intelligent Systems

ISSN

1541-1672

e-ISSN

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Svazek periodika

24

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

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Kód UT WoS článku

000266330000006

EID výsledku v databázi Scopus

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