Masters Theses

Monu Bambroo

Keywords and Phrases

Bucket brigade algorithm; C4.5 rule

Abstract

"An Intrusion Detection System should optimally be capable of detecting both known attacks (misuse detection) and unknown attacks (anomaly detection combined with non-self classification). This thesis research studies the problem of automating the generation of a high-fidelity ‘detection model’ that can recognize both known and variations on known attacks through the use of a Fuzzy Learning Classifier System. Experimental results on the classic KDDCup’99 benchmark dataset reveal that the proposed model outperforms published results obtained with the well-known C4.5 classification program. Fuzzy Logic and Evolutionary Computation are very robust in modeling real-world problems like intrusion detection. Therefore, the proposed model is aimed at using fuzzy rules for effective intrusion detection with the goal of evolving the rules over time with a Learning Classifier System. This approach is complemented with the optimization of the membership functions for the fuzzy rules using Evolutionary Algorithms. This hybrid approach was shown to significantly improve the accuracy of an Intrusion Detection System"--Abstract, page iii.

Advisor(s)

Tauritz, Daniel R.

Committee Member(s)

Liu, Xiaoqing Frank
Acar, Levent

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

University of Missouri--Rolla

Publication Date

Spring 2005

Pagination

x, 62 pages

Note about bibliography

Includes bibliographical references (pages 60-61).

Rights

© 2005 Monu Bambroo, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Subject Headings

Fuzzy logicComputer securityGenetic algorithms

Thesis Number

T 8785

Print OCLC #

62775537

Download
Off-campus S&T users

Share My Thesis If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.

Share

 
COinS