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Author:Alene, Henok
Title:Graph based clustering for anomaly detection in IP networks
Publication type:Master's thesis
Publication year:2011
Pages:[11] + 67      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Oja, Erkki
Instructor:Hätönen, Kimmo
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions
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Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

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    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

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Printing the thesis

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  • Other customers can use the printer u90203-psc3. All printing is subject to a charge to non-University members.
Location:P1 Ark Aalto  7071   | Archive
Keywords:anomaly detection
graphs
node degree
node clustering
weight matrix
partition
clustering
Abstract (eng): In IP networks, an anomaly detection system identifies attacks, device failures or other unknown processes that deviate from the normal behaviour of the network known as anomalies.
The thesis studied anomaly detection in traffic datasets from IP networks.
The datasets contained high number of normal events and few anomalies.
This resembles a normally operating network.

We construct graphs from traffic data and study their properties.
We formulated anomaly detection as a graph based clustering problem.
A novel graph bi-partitioning algorithm called NodeClustering was designed to separate normal samples from anomalous ones.

Performance of NodeClustering was investigated with extensive network traffic data.
The performance was compared with state of the art graph based spectral clustering algorithms.
NodeClustering identified all the known intrusions in the data and outperformed the compared graph based methods with an average improvement of 50% on the true positive rate with lowest false positive rate on the studied datasets.
In addition, its applicability to one non-traffic dataset was shown.

NodeClustering can be used in IP networks to detect anomalies.
In the future, threshold used for graph partitioning can be studied further and computationally efficient < methods to construct larger graphs might be studied.
ED:2011-12-14
INSSI record number: 43255
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