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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Side Channel Analysis of SPECK Based on Transfer Learning.

Qingqing Zhang1, Hongxing Zhang1, Xiaotong Cui1

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

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary

Researchers successfully attacked the SPECK lightweight encryption algorithm using deep learning side-channel analysis. They recovered 48 bits of the key by selecting XOR operation results as labels and employing transfer learning for key byte dependencies.

Keywords:
SPECKdeep learningintermediate operationside channel analysistransfer learning

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Area of Science:

  • Cryptography
  • Computer Science
  • Electrical Engineering

Background:

  • Deep learning side-channel attacks are prevalent for AES but underexplored for lightweight algorithms like SPECK.
  • Lightweight algorithms' reduced non-linear operations pose challenges for successful cryptanalysis.
  • Directly labeling initial keys in SPECK yields limited success (16-bit key recovery).

Purpose of the Study:

  • To investigate and enhance deep learning-based side-channel attacks on the SPECK lightweight encryption algorithm.
  • To identify optimal leakage labels for improved key recovery in SPECK.
  • To develop a novel method addressing inter-key byte dependencies in SPECK cryptanalysis.

Main Methods:

  • Evaluated leakage from SPECK operations (modular addition, XOR, shift).
  • Selected XOR operation results as labels for enhanced key recovery.
  • Utilized visualization to identify correlations between key bytes.
  • Proposed and applied a transfer learning approach for interdependent key bytes.

Main Results:

  • Successfully recovered the last 48 bits of the SPECK key by using XOR results as labels.
  • Identified significant overlap in electromagnetic leakage signals corresponding to different key bytes.
  • Demonstrated that transfer learning reduces model convergence time by 50% by leveraging key byte similarities.

Conclusions:

  • The XOR operation's leakage is a viable and effective label for SPECK side-channel attacks.
  • Key bytes in SPECK are not independent, necessitating advanced cryptanalysis techniques.
  • Transfer learning significantly improves the efficiency and performance of side-channel attacks on lightweight ciphers like SPECK.