Evolutionary Algorithm Based Automated Reverse Engineering and Defect Discovery

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 GP is an evolutionary algorithm that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense that it maximizes a fitness function. The system to be reverse engineered is typically a subcomponent of a sensor that may not be disassembled and for which there are no design documents. 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 GP, allowing GP-based data mining. 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. These design properties can be used to create a fitness function for a genetic algorithm (GA), which is in turn used to search for defects in the digital logic (DL) design. In this report, design flaws in two different sensor systems are detected using a GA. One of these systems makes passive detections, the other makes up part of a radar. In the second case, detecting the flaw allows the design of a radar jamming signal. Uncertainty related to the input-output database and the expert-based rule set can significantly alter the reverse engineering results. This report provides significant experimental and theoretical results related to GP-based data mining for reverse engineering. It presents methods of quantifying uncertainty. Finally, it examines methods for reducing the uncertainty.

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Document Details

Document Type
Technical Report
Publication Date
Sep 21, 2007
Accession Number
ADA472759

Entities

People

  • James F. Smith Iii

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Databases
  • Defect Detection
  • Detection
  • Detectors
  • Electronic Warfare
  • Engineering
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Measurement
  • Passive Sensors
  • Random Number Generators

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Space
  • Space - Space Objects