A windowing-based approach for intrusion detection

Author

Reno Madanmohan Kayarkar

Keywords and Phrases

Hamming distance; Fuzzy ART

Abstract

"Intrusion detection systems (IDS) attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. An IDS is often required to examine an enormous amount of data generated by computer networks to assist in the abuse detection process. The development of automated techniques is needed to address the requirements of IDS and to assist system administrators in the detection of existing security violations. This research investigates pattern recognition approaches for anomaly detection for insider system operations based on the analysis of a system's log files. For anomaly detection, concurrent event-based and modified Hamming distance features are examined. These features are evaluated using two machine learning approaches: 1) standard back propagation neural networks and 2) fuzzy adaptive resonance theory. The system examined for this research is the Boots system, an internally developed system, for controlling the movement of boots from one place to another under specific security restrictions related to the boot orders. The results of this work apply to intrusion detection results for log data collected from the Boots system. The experimental results are presented and compared to a benchmark event windowing technique"--Abstract, page iii.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Fall 2004

Pagination

viii, 89 pages

Rights

© 2004 Renu Madanmohan Kayarkar, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Neural networks (Computer science)Pattern recognition systemsComputer networks -- Security measuresFuzzy logic

Thesis Number

T 8685

Print OCLC #

61855085

Recommended Citation

Kayarkar, Reno Madanmohan, "A windowing-based approach for intrusion detection" (2004). Masters Theses. 4095.
https://scholarsmine.mst.edu/masters_theses/4095