Earticle

초록

영어

In this paper, we propose deep learning-based cryptanalysis on lightweight block ciphers, SIMON and SPECK. The block-sized bit arrays are encrypted with the block ciphers applying different number of round functions. The deep learning models are trained to generate the ciphertexts from the plaintexts and recover the plaintexts from the ciphertexts, which are attacks called Encryption Emulation (EE) and Plaintext Recovery (PR), respectively. The results are compared by using Bit Accuracy Probability (BAP) for each bit. It is shown that the round-reduced SIMON is higher BAP than round-reduced SPECK32/64. These results indicate that the round-reduced SIMON32/64 is more vulnerable than the round-reduced SPECK32/64.

목차

Abstract
1. Introduction
2. Methods
2.1. Dataset
2.2. Proposed method
3. Experiment result
4. Conclusions
Acknowledgement
References

키워드

저자

• Ongee Jeong [ Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) ]
• Inkyu Moon [ Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) ] Correspondence author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004