[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116917-en":3,"doc-seo-116917-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116917,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Classifying World War II Era Ciphers with Machine Learning","This Master’s Project evaluates whether machine learning and deep learning can classify World War II era ciphers using ciphertext only. The study covers Enigma, M-209, Sigaba, Purple, and Typex, extracting multiple feature representations such as raw ciphertext letter sequences, histograms, and n-grams. Classification is tested across two scenarios: fixed plaintext with fixed keys and random plaintext with fixed keys. Results indicate that histogram features perform best and classic machine learning approaches fit this categorization task more effectively.","San Jose State University  \nSJSU ScholarWorks  \n\n| Master's Projects | Master's Theses and Graduate Research |\n| --- | --- |\n| Spring 2023\u003Cbr>Classifying World War II Era Ciphers with Machine Learning Brooke Dalton\u003Cbr>San Jose State University\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/etd_projects](https://scholarworks.sjsu.edu/etd_projects)\u003Cbr> Part of the Information Security Commons |  |\n\nRecommended Citation  \nDalton, Brooke, \"Classifying World War II Era Ciphers with Machine Learning\" (2023) . Master 's Projects. 1272.  \nDOI: [https://doi.org/10.31979/etd.q29r-kkb6](https://doi.org/10.31979/etd.q29r-kkb6)  \n[https://scholarworks.sjsu.edu/etd_projects/1272](https://scholarworks.sjsu.edu/etd_projects/1272)  \nThis Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nClassifying World War II Era Ciphers with Machine Learning  \nA Project  \nPresented to  \nThe Faculty of the Department of Computer Science San José State University  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science  \nby  \nBrooke Dalton  \nMay 2023  \n© 2023  \nBrooke Dalton  \nALL RIGHTS RESERVED  \nThe Designated Project Committee Approves the Project Titled  \nClassifying World War II Era Ciphers with Machine Learning  \nby  \nBrooke Dalton  \nAPPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE  \nSAN JOSÉ STATE UNIVERSITY  \nMay 2023  \nDr. Mark Stamp  \nDr. Fabio Di Troia  \nDr. Genya Ishigaki  \nDepartment of Computer Science  \nDepartment of Computer Science  \nDepartment of Computer Science  \nABSTRACT  \nClassifying World War II Era Ciphers with Machine Learning  \nby Brooke Dalton  \nWe examine whether machine learning and deep learning techniques can classify World War II era ciphers when only ciphertext is provided. Among the ciphers considered are Enigma, M-209 , Sigaba, Purple, and Typex. For our machine learning models, we test a variety of features including the raw ciphertext letter sequence, histograms, and n-grams. The classification is approached in two scenarios. The first scenario considers fixed plaintext encrypted with fixed keys and the second scenario considers random plaintext encrypted with fixed keys. The results show that histograms are the best feature and classic machine learning methods are more appropriate for this kind of categorization.  \nACKNOWLEDGMENTS  \nI would like to thank my advisor Dr. Mark Stamp for encouraging me to experiment with new topics and for guiding me throughout the entirety of this research. His ideas, suggestions, and patience helped me immensely. I would also like to thank my committee members, Dr. Fabio Di Troia and Dr. Genya Ishigaki, for their time and feedback.  \nTABLE OF CONTENTS  \nCHAPTER  \n1 Introduction ................................ 1  \n2 Background ................................ 3  \n2.1 World War II Ciphers ........................ 3  \n2.1.1 Enigma ............................ 3  \n2.1.2 M-209 ............................. 4  \n2.1.3 Sigaba ............................. 5  \n2.1.4 Purple ............................. 6  \n2.1.5 Typex ............................. 8  \n2.2 Classic Machine Learning Algorithms ................ 9  \n2.2.1 Support Vector Machine ................... 9  \n2.2.2 􀁫-Nearest Neighbor ...................... 9  \n2.2.3 Random Forest Classifier ................... 10  \n2.3 Deep Learning Algorithms ...................... 10  \n2.3.1 Multilayer Perceptron .................... 10  \n2.3.2 Long Short-Term Memory .................. 11  \n2.3.3 Extreme Learning Machines ................. 11  \n2.3.4 Convolutional Neural Network ................ 12  \n2.4 Previous Work ............................ 12  \n3 Data set Preparation and Experimental Design .......... 13  \n3.1 Data Set .....................","cbCaif4VH9sZQBqV","https://ap.wps.com/l/cbCaif4VH9sZQBqV","pdf",7895467,1,135,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Background\n## 2.1 World War II Ciphers\n## 2.2 Classic Machine Learning Algorithms\n## 2.3 Deep Learning Algorithms\n## 2.4 Previous Work\n# Chapter 3 Data set Preparation and Experimental Design\n## 3.1 Data Set\n## 3.2 Feature Collection and Extraction\n## 3.3 Overview of Proposed Approach\n# Chapter 4 Results\n## 4.1 Histogram\n## 4.2 Digram\n## 4.3 Letter Sequence\n## 4.4 Effect of Ciphertext Length\n## 4.5 Discussion\n# Chapter 5 Conclusion and Future Work","[{\"question\":\"What is the main goal of this project?\",\"answer\":\"To determine whether machine learning and deep learning can classify World War II era ciphers when only ciphertext is available.\"},{\"question\":\"Which ciphers are included in the experiments?\",\"answer\":\"The experiments consider Enigma, M-209, Sigaba, Purple, and Typex.\"},{\"question\":\"What features and scenarios are used for classification?\",\"answer\":\"Models use raw ciphertext letter sequences, histograms, and n-grams, tested under scenarios with fixed plaintext and fixed keys versus random plaintext with fixed keys.\"}]","Classifying World War II Era Ciphers with Machine Learning | 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