Quantitative Metric and Automated Toolset for Obfuscated Logic Security Evaluation

Abstract

This program seeks to develop security criteria based on concepts in machine learning and cryptography and develop novel deobfuscation algorithms. Provably secure obfuscation strategies provide feasible solutions for the Department of Defense to protect critical system components, prevent IP violation, and address most of the supply chain privacy and security concerns.

Document Details

Document Type
DoD Grant Award
Publication Date
Jul 29, 2019
Source ID
FA86501817822

Entities

People

  • Yier Jin

Organizations

  • Air Force Research Laboratory
  • Defense Advanced Research Projects Agency
  • University of Florida

Tags

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Cyber