David Eckhoff, "Entwicklung von Anomaliedetektionsalgorithmen basierend auf Netflowdaten," Bachelor Thesis, Department of Computer Science, University of Erlangen-Nuremberg, September 2008. (Advisors: Tobias Limmer and Falko Dressler)
Abstract
Anomaly detection is an important tool to detect and respond to unknown malicious software in IP networks. Networks experiencing high traffic volume are not suited for real time detection using in depth packet analysis due to the required processing power. Flow analysis, i.e. analysing aggregated packet flows rather than packet content, presents a suitable alternative for real time anomaly detection in these high throughput networks. First an existing worm detection algorithm that classifies hosts based on the rate of new outbound connections is implemented and evaluated. Additionally the concept of aggregating hosts to subnets to perform an efficient analysis of the individual aggregated subnets based on various traffic properties to detect anomalies is proposed. It is then shown that worm detection based on the rate of outbound connections has several weaknesses. The second proposed approach detects several anomalies and is more suitable for realtime monitoring large networks.
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BibTeX reference
@phdthesis{eckhoff2008entwicklung,
author = {Eckhoff, David},
school = {University of Erlangen-Nuremberg},
title = {{Entwicklung von Anomaliedetektionsalgorithmen basierend auf Netflowdaten}},
year = {2008},
month = {September},
type = {Bachelor Thesis},
advisor = {Limmer, Tobias and Dressler, Falko},
institution = {Department of Computer Science},
location = {Erlangen, Germany},
}
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