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Related Experiment Video

Updated: Sep 14, 2025

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
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Deep learning-based profiling side-channel attacks in SPECK cipher.

Faisal Hameed1,2, Hoda Alkhzaimi3,4

  • 1Tandon School of Engineering, New York University, New York, USA. fah276@nyu.edu.

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|July 18, 2025
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Summary
This summary is machine-generated.

This study presents a new deep learning method for profiling side-channel attacks on the SPECK cipher. The technique successfully recovered the SPECK-32/64 key using minimal data, enhancing IoT security.

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Area of Science:

  • Cryptography
  • Computer Science
  • Machine Learning

Background:

  • Deep learning profiling side-channel analysis is advancing, particularly for classical ciphers like AES.
  • Adaptation of these advanced techniques to lightweight cryptography, such as the SPECK cipher, is underexplored.
  • SPECK is crucial for Internet of Things (IoT) devices, necessitating strong defenses against side-channel attacks.

Purpose of the Study:

  • To introduce a novel deep learning-based profiling side-channel analysis for the SPECK lightweight cipher.
  • To address the gap in research applying advanced deep learning attacks to lightweight primitives.
  • To evaluate the effectiveness of deep learning ensemble methods against SPECK implementations.

Main Methods:

  • Utilized a sequential divide and conquer ensemble of deep learning models.
  • Applied the technique to software implementations of the SPECK-32/64 cipher.
  • Focused on profiling side-channel analysis to recover the secret key.

Main Results:

  • Successfully recovered the 8-byte secret key of the SPECK-32/64 cipher.
  • Achieved key recovery in fewer than 250 traces, demonstrating high efficiency.
  • This represents the first known deep learning-based profiling attack on both unprotected and protected SPECK implementations.

Conclusions:

  • Deep learning ensemble methods are effective for profiling side-channel attacks on lightweight ciphers like SPECK.
  • The proposed method offers a significant advancement in securing resource-constrained devices in IoT.
  • Further research is warranted to explore these techniques on other lightweight cryptographic primitives.