First Advisor

Lupo, James A.

Second Advisor

Likarish, Daniel M.

Third Advisor

Hart, Douglas I.

College

College for Professional Studies

Degree Name

MS Information Assurance

School

School of Computer & Information Science

Document Type

Thesis - Open Access

Number of Pages

108 pages

Abstract

Examining payload content is an important aspect of network security, particularly in today's volatile computing environment. An Intrusion Detection System (IDS) that simply analyzes packet header information cannot adequately secure a network from malicious attacks. The alternative is to perform deep-packet analysis using n-gram language parsing and neural network technology. Self Organizing Map (SOM), PAYL over Self-Organizing Maps for Intrusion Detection (POSEIDON), Anomalous Payload-based Network Intrusion Detection (PAYL), and Anagram are next-generation unsupervised payload anomaly-based IDSs. This study examines the efficacy of each system using the design-science research methodology. A collection of quantitative data and qualitative features exposes their strengths and weaknesses.

Date of Award

Fall 2010

Location (Creation)

Denver, Colorado

Rights Statement

All content in this Collection is owned by and subject to the exclusive control of Regis University and the authors of the materials. It is available only for research purposes and may not be used in violation of copyright laws or for unlawful purposes. The materials may not be downloaded in whole or in part without permission of the copyright holder or as otherwise authorized in the “fair use” standards of the U.S. copyright laws and regulations.

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