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Proceedings Paper

Genetic program based data mining to reverse engineer digital logic
Author(s): James F. Smith; Thanh Vu H. Nguyen
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Paper Abstract

A data mining based procedure for automated reverse engineering and defect discovery has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts' rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Uncertainty related to the input-output database and the expert based rule set can significantly alter the reverse engineering results. Significant experimental and theoretical results related to GP based data mining for reverse engineering will be provided. Methods of quantifying uncertainty and its effects will be presented. Finally methods for reducing the uncertainty will be examined.

Paper Details

Date Published: 18 April 2006
PDF: 12 pages
Proc. SPIE 6241, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006, 624103 (18 April 2006); doi: 10.1117/12.664952
Show Author Affiliations
James F. Smith, Naval Research Lab. (United States)
Thanh Vu H. Nguyen, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 6241:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2006
Belur V. Dasarathy, Editor(s)

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