{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T18:13:44Z","timestamp":1789841624293,"version":"4.0.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902265"],"award-info":[{"award-number":["61902265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2016YFE0206700"],"award-info":[{"award-number":["2016YFE0206700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFB0804503"],"award-info":[{"award-number":["2018YFB0804503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author\u2019s writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model\u2019s accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa059","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T12:03:30Z","timestamp":1588593810000},"page":"1775-1787","source":"Crossref","is-referenced-by-count":3,"title":["EmailDetective: An Email Authorship Identification And Verification Model"],"prefix":"10.1093","volume":"63","author":[{"given":"Yong","family":"Fang","sequence":"first","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan 610022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan 610022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan 610022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"2020111609454745400_ref1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-00828-4_18","article-title":"Short Text Feature Extraction via Node Semantic Coupling and Graph Structures","volume-title":"Intelligent Information Processing IX","author":"Ma","year":"2018"},{"key":"2020111609454745400_ref2","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-13-3765-9_47","article-title":"Email Authorship Attribution","volume-title":"Innovations in Electronics and Communication Engineering","author":"Patil","year":"2019"},{"key":"2020111609454745400_ref3","first-page":"191","article-title":"A Graph Model Based Author Attribution Technique for Single-Class E-mail Classification","volume-title":"2015 IEEE\/ACIS 14th International Conference on Computer and Information Science (ICIS)","author":"Sohn","year":"2015"},{"key":"2020111609454745400_ref4","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1145\/1774088.1774428","article-title":"E-mail Authorship Verification for Forensic Investigation","volume-title":"SAC\u201910: The 2010 ACM Symposium on Applied Computing, Sierre Switzerland, March","author":"Iqbal","year":"2010"},{"key":"2020111609454745400_ref5","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1093\/llc\/fqm020","article-title":"Quantitative authorship attribution: an evaluation of techniques","volume":"22","author":"Grieve","year":"2007","journal-title":"Lit. 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