HKUST Library Institutional Repository Banner

HKUST Institutional Repository >
Computer Science and Engineering >
CSE Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2484
Title: Parzen-window network intrusion detectors
Authors: Yeung, Dit-Yan
Chow, Calvin
Keywords: Pattern classification
Data security
Gaussian kernels
Parzen-window estimators
Issue Date: 2002
Citation: Proceedings 16th International Conference on Pattern Recognition, 11-15 August 2002, vol. 4, p.385-388
Abstract: Network intrusion detection is the problem of detecting anomalous network connections caused by intrusive activities. Many intrusion detection systems proposed before use both normal and intrusion data to build their classifiers. However, intrusion data are usually scarce and difficult to collect. We propose to solve this problem using a novelty detection approach. In particular, we propose to take a nonparametric density estimation approach based on Parzen-window estimators with Gaussian kernels to build an intrusion detection system using normal data only. To facilitate comparison, we have tested our system on the KDD Cup 1999 dataset. Our system compares favorably with the KDD Cup winner which is based on an ensemble of decision trees with bagged boosting, as our system uses no intrusion data at all and much less normal data for training.
Rights: © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
URI: http://hdl.handle.net/1783.1/2484
Appears in Collections:CSE Conference Papers

Files in This Item:

File Description SizeFormat
yeung.icpr2002.pdfpre-published version74KbAdobe PDFView/Open

All items in this Repository are protected by copyright, with all rights reserved.