REHÁK, Martin, Michal PĚCHOUČEK, Martin GRILL, Jan STIBOREK, Karel BARTOŠ and Pavel ČELEDA. Adaptive Multiagent System for Network Traffic Monitoring. IEEE Intelligent Systems. Los Alamitos, CA, USA: IEEE Computer Society, 2009, vol. 24, No 3, p. 16-25. ISSN 1541-1672.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Adaptive Multiagent System for Network Traffic Monitoring
Name in Czech Adaptivní multiagentní systém pro sledování síťového provozu
Authors REHÁK, Martin (203 Czech Republic), Michal PĚCHOUČEK (203 Czech Republic), Martin GRILL (203 Czech Republic), Jan STIBOREK (203 Czech Republic), Karel BARTOŠ (203 Czech Republic) and Pavel ČELEDA (203 Czech Republic, guarantor, belonging to the institution).
Edition IEEE Intelligent Systems, Los Alamitos, CA, USA, IEEE Computer Society, 2009, 1541-1672.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 3.144
RIV identification code RIV/00216224:14610/09:00042538
Organization unit Institute of Computer Science
UT WoS 000266330000006
Keywords in English network intrusion detection; data mining; multiagent systems; trust
Tags data mining, multiagent systems, network intrusion detection, rivok, trust
Tags International impact, Reviewed
Changed by Changed by: doc. Ing. Pavel Čeleda, Ph.D., učo 206086. Changed: 14/3/2011 10:17.
Abstract
An application of agent-based data mining for near-real time detection of attacks against the computer networks and connected hosts is based on processing network traffic statistics provided by high-speed network monitoring cards and using 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. Using the agent-based trust modeling technique, the Camnep system fuses the data provided by anomaly-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.
Abstract (in Czech)
Adaptivní multiagentní systém pro sledování síťového provozu. Systém je založen na agentech využívájící samoadoptujících technik pro dynamické přizpůsobení struktury na základě změn síťového provozu a útoků.
Links
W911NF-08-1-0250, interní kód MUName: CAMNEP2 - Reflective-Cognitive Adaptation for Network Intrusion Detection Systems (Acronym: CAMNEP II)
Investor: U.S. Army RDECOM Acquisition Center, Reflective-Cognitive Adaptation for Network Intrusion Detection Systems
PrintDisplayed: 26/7/2024 11:39