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A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm.

Shaofei Sun1, Hongxin Zhang1, Liang Dong1,2

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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|December 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-objective electromagnetic analysis using a genetic algorithm to improve cryptographic key recovery. The enhanced method fully utilizes signal information, reducing required data traces for better efficiency.

Keywords:
Advanced Encryption Standard (AES)correlation electromagnetic analysisgenetic algorithmmulti-objective optimization

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Area of Science:

  • Cybersecurity
  • Applied Cryptography
  • Signal Processing

Background:

  • Correlation Electromagnetic Analysis (CEMA) is crucial for side-channel attacks on cryptographic devices.
  • Traditional CEMA methods often ignore information in non-target key bytes, potentially hindering key recovery.
  • Multi-objective optimization offers a potential solution to leverage information from all key bytes.

Purpose of the Study:

  • To develop an improved Correlation Electromagnetic Analysis (CEMA) method for cryptographic devices.
  • To integrate multi-objective optimization and genetic algorithms for enhanced key byte analysis.
  • To maximize the utilization of electromagnetic signal information during key recovery.

Main Methods:

  • Applied multi-objective optimization to Correlation Electromagnetic Analysis (CEMA).
  • Developed a novel multi-objective electromagnetic analysis method integrating genetic algorithms.
  • Conducted experiments using the Advanced Encryption Standard (AES) on a Sakura-G board.

Main Results:

  • The proposed method effectively analyzes all key bytes simultaneously.
  • Demonstrated a significant reduction in the number of required electromagnetic signal traces.
  • Achieved approximately 42.72% improvement in efficiency compared to standard CEMA.

Conclusions:

  • The novel multi-objective electromagnetic analysis based on a genetic algorithm enhances key recovery efficiency.
  • This approach provides a more comprehensive analysis by considering all key bytes.
  • The method proves practical and efficient for real-world cryptographic implementations like AES.