인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
Substitution ciphers-where characters are systematically replaced with different ones-have represented a vital tool in safeguarding sensitive information for centuries, eventually laying the foundation for modern encryption techniques. Despite their large key space, substitution ciphers are susceptible to frequency analysis that leverages common English letter patterns. While existing research has suggested certain methods that can improve decryption accuracy using ngram frequencies, these methods face difficulties when used with short ciphertexts due to incomplete letter distribution representation. The present study examines the limitations of current frequency analysis in decrypting short ciphertexts, with the results revealing that deterministic bigram approaches can reduce accuracy in certain cases. To address this shortcoming, we introduce a novel algorithm that uses randomized index selection based on letter distribution to generate multiple candidate keys. We also present a word-level key guessing method using these candidates that maps prominent English words to uncover a secret key. The results of tests with 200 ciphertexts of varying lengths showed an average decryption accuracy of 84.1% for 200-character ciphertexts, an improvement of 147.1% over existing methods. In experiments without dictionary-based decryption, an accuracy of 77.6% was achieved with a decryption time of approximately 0.27 seconds, which is a reasonable completion time. Altogether, these results highlight the efficiency and practicality of
our approach for decrypting short ciphertexts.
our approach for decrypting short ciphertexts.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-25-02-091192254
