NDLI: A hybrid approach to the profile creation and intrusion detection

Content Provider	IEEE Xplore Digital Library
Author	Marin, J. Ragsdale, D. Sirdu, J.
Copyright Year	2001
Description	Author affiliation: Inf. Technol. & Oper. Center, US Mil. Acad., West Point, NY, USA (Marin, J.)
Abstract	Anomaly detection involves characterizing the behaviors of individuals or systems and recognizing behavior that is outside the norm. This paper describes some preliminary results concerning the robustness and generalization capabilities of machine learning methods in creating user profiles based on the selection and subsequent classification of command line arguments. We base our method on the belief that legitimate users can be classified into categories based on the percentage of commands they use in a specified period. The hybrid approach we employ begins with the application of expert rules to reduce the dimensionality of the data, followed by an initial clustering of the data and subsequent refinement of the cluster locations using a competitive network called Learning Vector Quantization. Since Learning Vector Quantization is a nearest neighbor classifier, and new record presented to the network that lies outside a specified distance is classified as a masquerader. Thus, this system does not require anomalous records to be included in the training set.
Sponsorship	DARPA
Starting Page	69
Ending Page	76
File Size	717253
Page Count	8
File Format	PDF
ISBN	0769512127
DOI	10.1109/DISCEX.2001.932193
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intrusion detection Vector quantization Lifting equipment Information technology Character recognition Robustness Learning systems Nearest neighbor searches Authorization Frequency
Content Type	Text
Resource Type	Article

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