EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC

James F. Smith III, ThanhVu H. Nguyen

2006

Abstract

A data mining based procedure for automated reverse engineering 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. Significant mathematical formalism and experimental results related to GP based data mining for reverse engineering will be provided.

References

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  2. Koza J.R., Bennett F.H., Andre, D., and Keane, M.A., 1999, Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, Morgan Kaufmann Publishers, Chapter 2.
  3. Smith, J. F., 2003a. Fuzzy logic resource manager: decision tree topology, combined admissible regions and the self-morphing property, In: I. Kadar ed., Signal Processing, Sensor Fusion, and Target Recognition XII, April, Orlando, SPIE Proceedings, pp. 104-114.
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  6. Smith, J. F., and Nguyen; T. H., 2005. Data mining based automated reverse engineering and defect discovery, In: B. Dasarathy ed., Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security Vol. 5812, April, Orlando, SPIE Proceedings, pp. 232-242.
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Paper Citation


in Harvard Style

F. Smith III J. and H. Nguyen T. (2006). EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-972-8865-61-0, pages 107-113. DOI: 10.5220/0001212201070113


in Bibtex Style

@conference{icinco06,
author={James F. Smith III and ThanhVu H. Nguyen},
title={EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2006},
pages={107-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001212201070113},
isbn={978-972-8865-61-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC
SN - 978-972-8865-61-0
AU - F. Smith III J.
AU - H. Nguyen T.
PY - 2006
SP - 107
EP - 113
DO - 10.5220/0001212201070113