A Clustering-Based Unsupervised Approach to Anomaly Intrusion Detection

DOI

10.2991/3ca-13.2013.51How to use a DOI?

Keywords

anomaly based IDS, 2-means clustering, Recall, Precision, F1 measure, Dunn index, Davies-Bouldin index

Abstract

In the present paper a 2-means clustering-based anomaly detection technique is proposed. The presented method parses the set of training data, consisting of normal and anomaly data, and separates the data into two clusters. Each cluster is represented by its centroid - one of the normal observations, and the other - for the anomalies. The paper also provides appropriate methods for clustering, training and detection of attacks. The performance of the presented methodology is evaluated by the following methods: Recall, Precision and F1-measure. Measurements of performance are executed with Dunn index and Davies-Bouldin index.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Evgeniya Nikolova
AU  - Veselina Jecheva
PY  - 2013/04
DA  - 2013/04
TI  - A Clustering-Based Unsupervised Approach to Anomaly Intrusion Detection
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation
PB  - Atlantis Press
SP  - 202
EP  - 205
SN  - 1951-6851
UR  - https://doi.org/10.2991/3ca-13.2013.51
DO  - 10.2991/3ca-13.2013.51
ID  - Nikolova2013/04
ER  -