Browsing by Author "Dr. Mladen A. Vouk, Committee Co-Chair"

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Formalizing Computer Forensic Analysis: A Proof-Based Methodology
(2004-07-18) Sremack, Joseph; Dr. Mladen A. Vouk, Committee Co-Chair; Dr. Jun Xu, Committee Co-Chair; Dr. Peng Ning, Committee Member
Computer forensics is an important subject in the field of computer security. Impenetrably secure systems are not a reality - hundreds of thousands of security breaches are reported annually. When a security breach does occur, certain steps must be taken to understand what happened and how to recover from the incident, including data collection, analysis, and recovery. These responses to an incident comprise one part of computer forensics. A successful forensic investigation of any security breach requires a sound approach. Forensics literature provides a general model for conducting an investigation that can acts as a template for forensic investigations. The current literature, however, has primarily focused on two extremes of forensics: technical details and high-level procedural guidelines. By focusing on the extremes, many of the intermediate steps and logical conclusions that a forensic investigator must make are omitted. This omission leaves the burden of forming the logical structure of an investigation to the investigator. Such ad hoc approaches can lead to inefficient investigations with extraneous investigatory steps, and possibly less accurate results. This thesis explores the formalization of existing computer forensic analysis techniques such that a complete forensic investigation can be conducted in an efficient and meticulous manner. The formalization includes the use of high-level incident information to formulate a broad hypothesis about the entire incident. The hypothesis is then proven by performing a series of lower-level proofs - either by inductive or by deductive (axiomatic inductive) means - each of which acts as a premise for the overall incident hypothesis. The formalized analysis is then applied to actual forensic investigations to demonstrate its effectiveness. The formalized methodology and techniques presented in this thesis demonstrate how forensic investigations can be scientifically rigorous without sacrificing the necessary amount of creativity that is required for a complete investigation.
On Locally Invertible Encoders and Multidimensional Convolutional Codes
(2006-08-10) Lobo, Ruben Gerald; Dr. Mladen A. Vouk, Committee Co-Chair; Dr. Donald L. Bitzer, Committee Co-Chair; Dr. Brian L. Hughes, Committee Member; Dr. Alexandra Duel-Hallen, Committee Member; Dr. Ernest Stitzinger, Committee Member
Multidimensional (m-D) convolutional codes generalize the well known notion of a 1-D convolutional code defined over a univariate polynomial ring with coefficients in a finite field to multivariate polynomial rings. The more complicated structure of a multivariate polynomial ring when compared to a univariate one, however, makes the generalization nontrivial. While 1-D convolutional codes have been thoroughly understood and have wide applications in communication systems, the theory of m-D convolutional codes is still in its infancy, and these codes lack unified notation and practical implementation. This dissertation develops a sequence space approach for realizing m-D convolutional codes. While most of the existing research is focused on algebraic aspects, fundamental issues regarding practical implementation that are well developed and fairly straightforward in the 1-D case have remained undefined for m-D convolutional codes. In this dissertation we address some of these issues. We define a new notion of sequence space ordering and show that certain multivariate polynomial matrices which we call as locally invertible encoders, when transformed to the sequence space domain, have an invertible subsequence map between their input and output sequences. This subsequence map has a well defined structure that allows for the explicit construction of locally invertible encoders by performing elementary operations on the ground field without the use of any polynomial operations. We use the invertible subsequence map to introduce a novel method to encode and invert multidimensional sequences. We show that locally invertible encoders have good structural properties which make them a natural choice to generate multidimensional convolutional codes.