Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Electrical and Computer Engineering

Major

Information Assurance

First Advisor

Thomas E. Daniels

Second Advisor

Douglas W. Jacobson

Third Advisor

Brett M. Bode

Abstract

Network security research can benefit greatly from testing environments that are capable of generating realistic, repeatable and configurable background traffic. In order to conduct network security experiments on systems such as Intrusion Detection Systems and Intrusion Prevention Systems, researchers require isolated testbeds capable of recreating actual network environments, complete with infrastructure and traffic details. Unfortunately, due to privacy and flexibility concerns, actual network traffic is rarely shared by organizations as sensitive information, such as IP addresses, device identity and behavioral information can be inferred from the traffic. Trace data anonymization is one solution to this problem. The research community has responded to this sanitization problem with anonymization tools that aim to remove sensitive information from network traces, and attacks on anonymized traces that aim to evaluate the efficacy of the anonymization schemes. However there is continued lack of a comprehensive model that distills all elements of the sanitization problem in to a functional reference model.;In this thesis we offer such a comprehensive functional reference model that identifies and binds together all the entities required to formulate the problem of network data anonymization. We build a new information flow model that illustrates the overly optimistic nature of inference attacks on anonymized traces. We also provide a probabilistic interpretation of the information model and develop a privacy metric for anonymized traces. Finally, we develop the architecture for a highly configurable, multi-layer network trace collection and sanitization tool. In addition to addressing privacy and flexibility concerns, our architecture allows for uniformity of anonymization and ease of data aggregation.

DOI

https://doi.org/10.31274/rtd-180813-16536

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Shantanu Gattani

Language

en

Proquest ID

AAI1453120

OCLC Number

235949616

ISBN

9780549541905

File Format

application/pdf

File Size

65 pages

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