Earticle

초록

영어

In this study, we consider application of deep learning methods in the cryptanalysis of tiny DES algorithm, which is a DES-like cipher. We develop two types of deep learning architectures to perform the cryptanalysis of tiny DES. It is a known-plaintext attack where the deep learning models only need ciphertext and plaintext pair as training and the learning target is to predict correct plaintext when a ciphertext is given. Simulation results have shown that deep learning methods cannot 100% recover the plaintext of tiny DES but can greatly reduce the analysis difficulty for plaintext recovery.

목차

Abstract
I. INTRODUCTION
II. TINY DES
III. DEEP LEARNING ARCHITECTURES
IV. SIMULATION RESULTS
V. CONCLUSIONS
REFERENCES

키워드

저자

• Ongee Jeong [ Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu, Republic of Korea ]
• Chung Ghiu Lee [ Department of Electronic Engineering Chosun University Gwangju, Republic of Korea ]
• Inkyu Moon [ Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004