NDLI: Data preprocessing for distance-based unsupervised Intrusion Detection

Content Provider	IEEE Xplore Digital Library
Author	Said, D. Stirling, L. Federolf, P. Barker, K.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, University of Calgary, AB, Canada (Said, D.; Barker, K.) \|\| Faculty of Kinesiology, University of Calgary, AB, Canada (Stirling, L.; Federolf, P.)
Abstract	Since Intrusion Detection Systems (IDSs) operate in real-time, they should be light-weighted to detect intrusions as fast as possible. Distance-based Outlier Detection (DBOD) is one of the most widely-used techniques for detecting outliers due to its simplicity and efficiency. Additionally, DBOD is an unsupervised approach which overcomes the problem of the lack of training datasets with known intrusions. However, since IDSs usually have high-dimensional datasets, using DBOD becomes subject to the curse of the dimensionality problem. Furthermore, intrusion datasets should be normalized before calculating pair-wise distance between observations. The purpose of this research is conduct a comparative study among different normalization methods in conjunction with a well-known feature extraction technique; Principle Component Analysis (PCA). Therefore, the efficiency of these methods as data preprocessing techniques can be investigated when applying DBOD to detect intrusions. Experiments were performed using two kinds of distance metrics; Euclidean distance and Mahalanobis distance. We further examined the PCA using 7 threshold values to indicate the number of Principle components to consider according to their total contribution in the variability of features. These approaches have been evaluated using the KDD Cup 1999 intrusion detection (KDD-Cup) dataset. The main purpose of this study is to find the best attribute normalization method along with the correct threshold value for PCA so that a fast unsupervised IDS can discover intrusions effectively. The results recommended using the Log normalization method combined the Euclidean distance while performing PCA.
Starting Page	181
Ending Page	188
File Size	1029705
Page Count	8
File Format	PDF
ISBN	9781457705823
e-ISBN	9781457705847
DOI	10.1109/PST.2011.5971981
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-19
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Intrusion detection Euclidean distance Feature extraction Equations Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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