Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Principal Component Analysis of Port-scans for Reduction of Distributed Sensors
Hiroaki KikuchiMasato Terada
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2010 Volume 18 Pages 190-200


There are many studies aimed at using port-scan traffic data for fast and accurate detection of rapidly spreading worms. This paper proposes two new methods for reducing the traffic data to a simplified form comprising of significant components of smaller dimensionality. (1) Dimension reduction via Principal Component Analysis (PCA), widely used as a tool in exploratory data analysis, enables estimation of how uniformly the sensors are distributed over the reduced coordinate system. PCA gives a scatter plot for the sensors, which helps to detect abnormal behavior in both the source address space and the destination port space. (2) One of the significant applications of PCA is to reduce the number of sensors without losing the accuracy of estimation. Our proposed method based on PCA allows redundant sensors to be discarded and the number of packets estimated even when half of the sensors are unavailable with accuracy of less than 3% of the total number of packets. In addition to our proposals, we report on experiments that use the Internet Scan Data Acquisition System (ISDAS) distributed observation data from the Japan Computer Emergency Response Team (JPCERT).

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© 2010 by the Information Processing Society of Japan
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