{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T04:48:41Z","timestamp":1772599721755,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Speech Technol"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10772-024-10110-y","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T18:02:46Z","timestamp":1718388166000},"page":"405-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A robust approach to authorship verification using siamese deep learning: application in phishing email detection"],"prefix":"10.1007","volume":"27","author":[{"given":"Mohamed Abdelkarim","family":"Remmide","sequence":"first","affiliation":[]},{"given":"Fatima","family":"Boumahdi","sequence":"additional","affiliation":[]},{"given":"Imane Rebeh","family":"Ammar Aouchiche","sequence":"additional","affiliation":[]},{"given":"Amina","family":"Guendouz","sequence":"additional","affiliation":[]},{"given":"Narhimene","family":"Boustia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"10110_CR1","doi-asserted-by":"crossref","unstructured":"Alhogail, A., & Alsabih, A. (2021). Applying machine learning and natural language processing to detect phishing email. Computers  &  Security. 110, 102414.","DOI":"10.1016\/j.cose.2021.102414"},{"key":"10110_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10361-2","author":"S Alterkav\u0142","year":"2021","unstructured":"Alterkav\u0142, S., & Erbay, H. (2021). Novel authorship verification model for social media accounts compromised by a human. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-020-10361-2","journal-title":"Multimedia Tools and Applications"},{"key":"10110_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8869681","author":"S Alterkav\u0142","year":"2021","unstructured":"Alterkav\u0142, S., & Erbay, H. (2021). Design and analysis of a novel authorship verification framework for hijacked social media accounts compromised by a human. Security and Communication Networks. https:\/\/doi.org\/10.1155\/2021\/8869681","journal-title":"Security and Communication Networks"},{"key":"10110_CR4","unstructured":"Anti-Phishing Working Group. (2021). Phishing activity trends report 4th quarter 2023."},{"key":"10110_CR5","doi-asserted-by":"publisher","DOI":"10.47150\/jstem010","author":"M Awan","year":"2020","unstructured":"Awan, M. (2020). Pishing attacks in network security. LC International Journal of STEM. https:\/\/doi.org\/10.47150\/jstem010","journal-title":"LC International Journal of STEM"},{"key":"10110_CR6","doi-asserted-by":"publisher","unstructured":"Benenson, Z., Gassmann, F., & Landwirth, R. (2017). Unpacking spear phishing susceptibility. In Brenner, M., et al. Financial cryptography and data security (FC 2017). Lecture Notes in Computer Science, vol 10323. Springer. https:\/\/doi.org\/10.1007\/978-3-319-70278-0_39","DOI":"10.1007\/978-3-319-70278-0_39"},{"key":"10110_CR7","doi-asserted-by":"crossref","unstructured":"Boenninghoff, B., Hessler, S., Kolossa, D., & Nickel, R.M. (2019). Explainable authorship verification in social media via attention-based similarity learning. In 2019 IEEE international conference on big data (Big Data), (pp. 36\u201345). IEEE","DOI":"10.1109\/BigData47090.2019.9005650"},{"key":"10110_CR8","doi-asserted-by":"publisher","unstructured":"Boenninghoff, B., Nickel, R.M., Zeiler, & S., Kolossa, D. (2019) Similarity learning for authorship verification in social media. In ICASSP 2019 - 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. https:\/\/doi.org\/10.1109\/icassp.2019.8683405","DOI":"10.1109\/icassp.2019.8683405"},{"key":"10110_CR9","doi-asserted-by":"publisher","unstructured":"Bountakas, P., & Xenakis, C. (2023). Helphed: Hybrid ensemble learning phishing email detection. Journal of Network and Computer Applications. https:\/\/doi.org\/10.1016\/j.jnca.2022.103545","DOI":"10.1016\/j.jnca.2022.103545"},{"key":"10110_CR10","unstructured":"Farazmanesh, F., Foroutan, F., & Bidgoly, A. J. (2022) Compromised account detection using authorship verification: a novel approach. arXiv:2206.03581 [cs.CR]"},{"key":"10110_CR11","doi-asserted-by":"crossref","unstructured":"Giorgi, G., Saracino, A., & Martinelli, F. (2020). Email spoofing attack detection through an end to end authorship attribution system. In 6th international conference on information systems security and privacy (ICISSP), (pp. 64\u201374).","DOI":"10.5220\/0008954600640074"},{"key":"10110_CR12","doi-asserted-by":"publisher","unstructured":"Halvani, O., Winter, C., & Graner, L. (2019) Assessing the applicability of authorship verification methods. In Proceedings of the 14th international conference on availability, reliability and security (ARES \u201919). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3339252.3340508","DOI":"10.1145\/3339252.3340508"},{"key":"10110_CR13","doi-asserted-by":"publisher","first-page":"98398","DOI":"10.1109\/ACCESS.2021.3095730","volume":"9","author":"M Hina","year":"2021","unstructured":"Hina, M., Ali, M., Javed, A. R., Ghabban, F., Khan, L. A., & Jalil, Z. (2021). Sefaced: Semantic-based forensic analysis and classification of e-mail data using deep learning. IEEE Access, 9, 98398\u201398411.","journal-title":"IEEE Access"},{"key":"10110_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3095730","author":"M Hina","year":"2021","unstructured":"Hina, M., Ali, M., Javed, A. R., Ghabban, F., Khan, L. A., & Jalil, Z. (2021). Sefaced: Semantic-based forensic analysis and classification of e-mail data using deep learning. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2021.3095730","journal-title":"IEEE Access"},{"key":"10110_CR15","doi-asserted-by":"crossref","unstructured":"Hina, M., Ali, M., Javed, A.R., Srivastava, G., Gadekallu, T.R., & Jalil, Z. (2021). Email classification and forensics analysis using machine learning. In 2021 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, internet of people and smart city innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/IOP\/SCI), (pp. 630\u2013635). IEEE","DOI":"10.1109\/SWC50871.2021.00093"},{"key":"10110_CR16","unstructured":"Hosseinia, M., & Mukherjee, A. (2018) Experiments with neural networks for small and large scale authorship verification."},{"key":"10110_CR17","doi-asserted-by":"crossref","unstructured":"Hung, C.-Y., Hu, Z., Hu, Y., & Lee, R.K.-W. (2023) Who wrote it and why? prompting large-language models for authorship verification. arXiv preprint. arXiv:2310.08123","DOI":"10.18653\/v1\/2023.findings-emnlp.937"},{"key":"10110_CR18","doi-asserted-by":"crossref","unstructured":"Iqbal, F., Javed, A.R., Jhaveri, R.H., Almadhor, A., & Farooq, U. (2023) Transfer learning-based forensic analysis and classification of e-mail content. ACM Transactions on Asian and Low-Resource Language Information Processing.","DOI":"10.1145\/3604592"},{"key":"10110_CR19","doi-asserted-by":"crossref","unstructured":"Juola, P. (2021). Verifying authorship for forensic purposes: A computational protocol and its validation. Forensic Science International, 325, 110824.","DOI":"10.1016\/j.forsciint.2021.110824"},{"issue":"1","key":"10110_CR20","first-page":"486","volume":"10","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Chatterjee, J. M., D\u00edaz, V. G., et al. (2020). A novel hybrid approach of svm combined with nlp and probabilistic neural network for email phishing. International Journal of Electrical and Computer Engineering, 10(1), 486.","journal-title":"International Journal of Electrical and Computer Engineering"},{"key":"10110_CR21","doi-asserted-by":"crossref","unstructured":"Leekha, R., & Vandam, C. (2023) A generalized solution to verify authorship and detect style change in multi-authored documents. In Proceedings of the international conference on advances in social networks analysis and mining. (pp. 652\u2013657).","DOI":"10.1145\/3625007.3627589"},{"key":"10110_CR22","unstructured":"Liu, X., Kong, L., & Huang, M. (2023) Text-segment interaction for authorship verification using BERT-based classification. Working Notes of CLEF"},{"key":"10110_CR23","unstructured":"Nini, A., Halvani, O., Graner, L., Gherardi, V., & Ishihara, S. (2024). Authorship verification based on the likelihood ratio of grammar models."},{"key":"10110_CR24","unstructured":"Overview of the authorship verification task at pan 2022.\u00a0(2022). CEUR workshop proceedings. 3180, 2301\u20132313."},{"key":"10110_CR25","first-page":"1707","volume":"4","author":"Parvinder","year":"2017","unstructured":"Parvinder. (2017). Cyber crimes in india: An overview. International Journal of Research., 4, 1707\u20131709.","journal-title":"International Journal of Research."},{"key":"10110_CR26","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s11063-017-9593-7","volume":"46","author":"S Seifollahi","year":"2017","unstructured":"Seifollahi, S., Bagirov, A., Layton, R., & Gondal, I. (2017). Optimization based clustering algorithms for authorship analysis of phishing emails. Neural Processing Letters, 46, 411\u2013425.","journal-title":"Neural Processing Letters"},{"key":"10110_CR27","doi-asserted-by":"crossref","unstructured":"Shrestha, P., Sierra, S., Gonz\u00e1lez, F., Montes, M., Rosso, P., & Solorio, T. (2017) Convolutional neural networks for authorship attribution of short texts. In Lapata, M., Blunsom, P., Koller, A. (Eds.) Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 2, short papers, (pp. 669\u2013674). Association for Computational Linguistics, Valencia, Spain. https:\/\/aclanthology.org\/E17-2106","DOI":"10.18653\/v1\/E17-2106"},{"key":"10110_CR300","doi-asserted-by":"crossref","unstructured":"Thomas, M., & Meshram, B. (2024). Optimizing hyperparameters for enhanced email classification and forensic analysis with stacked autoencoders. International Journal of Network Security & Its Applications 16(1), 21\u201333.","DOI":"10.5121\/ijnsa.2024.16102"},{"key":"10110_CR29","unstructured":"Weerasinghe, J., & Greenstadt, R. (2020). Feature vector difference based neural network and logistic regression models for authorship verification. In CEUR workshop proceedings, vol. 2695."}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-024-10110-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10772-024-10110-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-024-10110-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T16:07:44Z","timestamp":1721664464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10772-024-10110-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["10110"],"URL":"https:\/\/doi.org\/10.1007\/s10772-024-10110-y","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"value":"1381-2416","type":"print"},{"value":"1572-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"23 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}