Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Department

Electrical Engineering

First Advisor's Name

Malek Adjouadi

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Naphtali Rishe

Third Advisor's Name

Ana Pasztor

Fourth Advisor's Name

Robert Oikawa

Fifth Advisor's Name

Malcolm Heimer

Sixth Advisor's Name

Tadeusz Babij

Seventh Advisor's Name

Melvin Ayala

Date of Defense

11-12-2004

Abstract

Security remains a top priority for organizations as their information systems continue to be plagued by security breaches. This dissertation developed a unique approach to assess the security risks associated with information systems based on dynamic neural network architecture. The risks that are considered encompass the production computing environment and the client machine environment. The risks are established as metrics that define how susceptible each of the computing environments is to security breaches.

The merit of the approach developed in this dissertation is based on the design and implementation of Artificial Neural Networks to assess the risks in the computing and client machine environments. The datasets that were utilized in the implementation and validation of the model were obtained from business organizations using a web survey tool hosted by Microsoft. This site was designed as a host site for anonymous surveys that were devised specifically as part of this dissertation. Microsoft customers can login to the website and submit their responses to the questionnaire.

This work asserted that security in information systems is not dependent exclusively on technology but rather on the triumvirate people, process and technology. The questionnaire and consequently the developed neural network architecture accounted for all three key factors that impact information systems security.

As part of the study, a methodology on how to develop, train and validate such a predictive model was devised and successfully deployed. This methodology prescribed how to determine the optimal topology, activation function, and associated parameters for this security based scenario. The assessment of the effects of security breaches to the information systems has traditionally been post-mortem whereas this dissertation provided a predictive solution where organizations can determine how susceptible their environments are to security breaches in a proactive way.

Identifier

FI14062261

Comments

If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to dcc@fiu.edu and include clear identification of the work, preferably with URL.

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