NDLI: An unsupervised network anomaly detection approach by k-Means clustering & ID3 algorithms

Content Provider	IEEE Xplore Digital Library
Author	Yasami, Y. Khorsandi, S. Mozaffari, S.P. Jalalian, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Eng., Amirkabir Univ. of Technol. (AUT), Tehran (Yasami, Y.; Khorsandi, S.; Mozaffari, S.P.)
Abstract	This paper presents a novel method to combine k-means clustering and ID3 decision trees learning algorithms for unsupervised classification of anomalous and normal activities in computer network ARP traffic. The k-means clustering method is first applied to the normal training instances to partition it into k clusters using Euclidean distance similarity. Some anomaly criteria has been defined and applied to the captured ARP traffic to generate normal training instances. An ID3 decision tree is constructed on each cluster. Anomaly scores from the k-means clustering algorithm and decisions of the ID3 decision trees are extracted. A special algorithm is used to combine results of the two algorithms and obtain final anomaly score values. The threshold rule is applied for making decision on the test instance normality or abnormality. Experimental results show that the proposed approach has a high precision, sensitivity and performance.
Starting Page	398
Ending Page	403
File Size	1337460
Page Count	6
File Format	PDF
ISBN	9781424427024
ISSN	15301346
DOI	10.1109/ISCC.2008.4625717
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-06
Publisher Place	Morocco
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Decision trees Training Classification algorithms Distance measurement Partitioning algorithms IP networks ID3 Decision Trees Anomaly Detection System (ADS) Address Resolution Protocol (ARP) Unsupervised Classification K-Means Clustering
Content Type	Text
Resource Type	Article
Subject	Signal Processing Mathematics Computer Networks and Communications Computer Science Applications Software

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