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Adaptive Multiagent System for Network Traffic Monitoring
May/June 2009 (vol. 24 no. 3)
pp. 16-25
ASCII Text
x 
 
Martin Rehák, Michal Pechoucek, Martin Grill, Jan Stiborek, Karel Bartoš, Pavel Celeda, "Adaptive Multiagent System for Network Traffic Monitoring," IEEE Intelligent Systems, vol. 24, no. 3, pp. 16-25, May/June, 2009.
 
 
BibTex
x
 
@article{ 10.1109/MIS.2009.42,
author = {Martin Rehák and Michal Pechoucek and Martin Grill and Jan Stiborek and Karel Bartoš and Pavel Celeda},
title = {Adaptive Multiagent System for Network Traffic Monitoring},
journal ={IEEE Intelligent Systems},
volume = {24},
number = {3},
issn = {1541-1672},
year = {2009},
pages = {16-25},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2009.42},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Adaptive Multiagent System for Network Traffic Monitoring
IS - 3
SN - 1541-1672
SP16
EP25
EPD - 16-25
A1 - Martin Rehák,
A1 - Michal Pechoucek,
A1 - Martin Grill,
A1 - Jan Stiborek,
A1 - Karel Bartoš,
A1 - Pavel Celeda,
PY - 2009
KW - network intrusion detection
KW - data mining
KW - multiagent systems
KW - trust
VL - 24
JA - IEEE Intelligent Systems
ER -
 
Martin Rehák, Czech Technical University
Michal Pechoucek, Czech Technical University
Martin Grill, Czech Technical University
Jan Stiborek, Czech Technical University
Karel Bartoš, Czech Technical University
Pavel Celeda, Masaryk University
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.

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Index Terms:
network intrusion detection, data mining, multiagent systems, trust
Citation:
Martin Rehák, Michal Pechoucek, Martin Grill, Jan Stiborek, Karel Bartoš, Pavel Celeda, "Adaptive Multiagent System for Network Traffic Monitoring," IEEE Intelligent Systems, vol. 24, no. 3, pp. 16-25, May-June 2009, doi:10.1109/MIS.2009.42
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