Automatic Feature Selection and Improved Classification in SICADA Counterfeit Electronics Detection

Abstract

Counterfeiters seeking financial gain can introduce misrepresented or recycled microelectronic components to both government and commercial supply chains. This reduces system reliability and trust, and currently has few comprehensive and practical solutions. The SICADA methodology was developed to detect such counterfeit microelectronics by collecting power side channel data and applying machine learning to identify counterfeits. This methodology has been extended to include a two-step automated feature selection process and now uses a one-class SVM classifier. We describe this methodology and show results for empirical data collected from several types of Microchip dsPIC33F microcontrollers.

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Document Details

Document Type
Technical Report
Publication Date
Mar 20, 2017
Accession Number
AD1041821

Entities

People

  • Eric Koziel
  • Keith Bergevin
  • Lauren Milechin
  • Michael Vai
  • Phil Comer

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Anomaly Detection
  • Change Detection
  • Classification
  • Coefficients
  • Counterfeit Detection
  • Demographic Cohorts
  • Detection
  • Discriminate Analysis
  • Feature Selection
  • Identification
  • Information Science
  • Machine Learning
  • Measurement
  • Power Measurement
  • Supervised Machine Learning
  • Supply Chain Integrity
  • Test And Evaluation

Readers

  • Cybersecurity.
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Microelectronics