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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12970

Title: COMPARATIVE ANALYSIS OF K-MEANS DATA MINING AND OUTLIER DETECTION APPROACH FOR NETWORK-BASED INTRUSION DETECTION
Authors: Kwao, Lazarus
Panford, Joseph Kobina
Hayfron-Acquah, James Ben
Keywords: K-Means
Outlier Detection Approach
Intrusion Detection
Network- based
NOF
clusters
Issue Date: Apr-2018
Publisher: International Journal of Computer Science and Information Security
Citation: International Journal of Computer Science and Information Security,Vol. 16, No. 4
Abstract: New kind of intrusions causes deviation in the normal behaviour of traffic flow in computer networks every day. This study focused on enhancing the learning capabilities of IDS to detect the anomalies present in a network traffic flow by comparing the k-means approach of data mining for intrusion detection and the outlier detection approach. The k-means approach uses clustering mechanisms to group the traffic flow data into normal and abnormal clusters. Outlier detection calculates an outlier score (neighbourhood outlier factor (NOF)) for each flow record, whose value decides whether a traffic flow is normal or abnormal. These two methods were then compared in terms of various performance metrics and the amount of computer resources consumed by them. Overall, k-means was more accurate and precise and has better classification rate than outlier detection in intrusion detection using traffic flows. This will help systems administrators in their choice of IDS.
Description: This article is published in International Journal of Computer Science and Information Security and also available at DOI: 10.2139/ssrn.3498169
URI: 10.2139/ssrn.3498169
http://hdl.handle.net/123456789/12970
Appears in Collections:College of Science

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