Masters Theses

Abstract

“Intrusion detection systems (IDS) provide an attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. The intrusions from inside an organization pose the toughest challenge to the IDS. In a distributed system, the amount of data processed is enormous and it has become impossible to analyze the data using simple manual analysis. There arises a need for automated tools that can detect anomalous behavior effectively. The IDS presented in this research performs anomaly detection using Adaptive Resonance Theory (ART1) clustering. The research uses vector time stamp log data generated from the BOOTS system for two different task types. The BOOTS system is a distributed system that controls the flow of boots from one place to another under a set of security considerations. The data used for task discrimination consists of a window of events drawn around a concurrent pair of events. The methods used to obtain the concurrent data, algorithms used and the results obtained are discussed”--Abstract, page iii.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

McMillin, Bruce M.
Miller, Ann K.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Publisher

University of Missouri--Rolla

Publication Date

Spring 2004

Pagination

viii, 63 pages

Note about bibliography

Includes bibliographical references (pages 61-62).

Rights

© 2004 Nageswaran Jayaraman, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Subject Headings

Computer networks -- Security measuresComputer securityNeural networks (Computer science)

Thesis Number

T 8432

Print OCLC #

55224909

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