딥러닝을 활용한 경량 블록 암호의 신경망 구분자 성능 개선 기술 연구
Techniques for Enhancing Neural Network Classifiers in Lightweight Block Ciphers Using Deep Learning
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초록
디지털 기술의 빠른 발전으로 보안의 중요성이 더욱 강조되면서, 민감한 데이터를 보호하기 위한 암호화 기술의 필요성이 커지고 있다. 그중, 경량 블록 암호 알고리즘은 자원 제약이 큰 IoT 기기와 같은 환경에서 주로 사용되며, 이러한 알고리즘의 안전성 평가를 위해 딥러닝 기술을 활용한 연구가 주목받고 있다. 특히, 차분 분석 공격에 신경망을 적용한 신경망 구분자(neural distinguisher)가 제안되면서 암호의 취약점을 효과적으로 식별하려는 시도가 늘고 있다. 그러나 암호 라운드가 증가할수록 성능이 저하되는 한계가 존재하게 되었고, 이를 해결하기 위해 입력 데이터 형식 및 가중치 함수 적용 등 다양한 성능 개선 기술이 제안되고 있다. 최근에는 암호문 쌍의 차분 뿐만 아니라 키 쌍의 차분을 이용한 연관키 신경망 구분자(related-key neural distinguisher)가 제안되면서 구분자의 성능이 향상됨을 보였다. 그러나 연관키 신경망 구분자의 성능 개선을 위한 연구는 아직 미흡한 수준이다. 이에 본 논문에서는 신경망 구분자의 성능을 향상시키기 위해 제안된 입력 데이터 형식과 가중치 함수의 영향을 분석하고, 구분자의 출력값 분포 특성 차이가 미치는 영향에 대해 탐구한다. 이를 통해 Simeck 32/64 알고리즘에 적합한 가중치 함수를 선정하며, 연관키 신경망 구분자의 성능 향상 방안을 도출하여 구분자의 종류에 따라 적합한 가중치 함수가 달라질 수 있음을 새로운 분석 관점으로 제시한다.
Abstract
The need for secure encryption is growing as digital technology advances. Lightweight block cipher algorithms, essential for resource-limited environments like IoT, are increasingly analyzed using deep learning. Neural distinguishers applying neural networks to differential crypanalysis have been proposed to identify vulnerabilities effectively, though they suffer performance drops with more encryption rounds. To address this, enhancements like modifications in input data formats and the use of weight functions have been proposed. Recently, related-key neural distinguishers, which utilize differences in both ciphertext pairs and key pairs, have demonstrated improved performance. Despite these advancements, studies focusing on performance optimization for related-key neural distinguishers are still limited. In this paper, we analyze the impact of input data formats and weight functions proposed to improve the performance of neural distinguishers and explore how differences in output value distribution characteristics affect performance. Through this analysis, we identify a suitable weight function for the Simeck 32/64 algorithm and propose methods to enhance the performance of related-key neural distinguishers, presenting a new analytical perspective that the optimal weight function may vary depending on the type of distinguisher.
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