{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:17:52Z","timestamp":1776442672467,"version":"3.51.2"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03532-7","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T08:22:42Z","timestamp":1737966162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Detecting Smishing Messages Using BERT and Advanced NLP Techniques"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9482-6991","authenticated-orcid":false,"given":"Ankit Kumar","family":"Jain","sequence":"first","affiliation":[]},{"given":"Kamaljeet","family":"Kaur","sequence":"additional","affiliation":[]},{"given":"Naveen Kumar","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Ankit","family":"Khare","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"issue":"3","key":"3532_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01078-0","volume":"3","author":"S Mishra","year":"2022","unstructured":"Mishra S, Soni D. Implementation of \u2018Smishing detector\u2019: an efficient model for Smishing Detection using neural network. SN Comput Sci. 2022;3(3):1\u201313. https:\/\/doi.org\/10.1007\/s42979-022-01078-0.","journal-title":"SN Comput Sci"},{"key":"3532_CR2","doi-asserted-by":"publisher","DOI":"10.1145\/3538491","author":"C Oswald","year":"2022","unstructured":"Oswald C, Simon SE, Bhattacharya A. SpotSpam: Intention Analysis-driven SMS spam detection using BERT Embeddings. ACM Trans Web. 2022 https:\/\/doi.org\/10.1145\/3538491.","journal-title":"ACM Trans Web"},{"key":"3532_CR3","unstructured":"Tatango. 90% of SMS Marketing Messages Read in 3 Minutes, tatango, 2020. https:\/\/www.tatango.com\/blog\/90-of-text-messages-are-read-within-3-minutes\/ (accessed Dec. 24, 2023)."},{"key":"3532_CR4","unstructured":"Pemberton C. Tap Into The Marketing Power of SMS, Gartner, 2016. https:\/\/www.gartner.com\/en\/marketing\/insights\/articles\/tap-into-the-marketing-power-of-sms (accessed Dec. 12, 2023)."},{"key":"3532_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.procs.2022.01.012","volume":"199","author":"ON Akande","year":"2021","unstructured":"Akande ON, Akande HB, Kayode AA, Adeyinka AA, Olaiya F, Oluwadara G. Development of a Real Time Smishing Detection Mobile Application using rule based techniques. Procedia Comput Sci. 2021;199:95\u2013102. https:\/\/doi.org\/10.1016\/j.procs.2022.01.012.","journal-title":"Procedia Comput Sci"},{"issue":"8","key":"3532_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s23083861","volume":"23","author":"A Ghourabi","year":"2023","unstructured":"Ghourabi A, Alohaly M. Enhancing Spam Message Classification and Detection Using Transformer-Based Embedding and Ensemble Learning. Sensors. 2023;23(8):1\u201317. https:\/\/doi.org\/10.3390\/s23083861.","journal-title":"Sensors"},{"key":"3532_CR7","unstructured":"Smitha C. A survey paper on smishing prediction system.\u00a0Int J Res Eng Sci. 2023;11(4):470\u20133.\u00a0https:\/\/www.ijres.org\/papers\/Volume-11\/Issue-4\/1104470473.pdf."},{"key":"3532_CR8","doi-asserted-by":"publisher","DOI":"10.24203\/ijcit.v11i1.201","author":"DN Njuguna","year":"2022","unstructured":"Njuguna DN, Kamau J, Kaburu D. A Review of Smishing Attaks Mitigation Strategies. Int J Comput Inf Technol. 2022. https:\/\/doi.org\/10.24203\/ijcit.v11i1.201.","journal-title":"Int J Comput Inf Technol"},{"key":"3532_CR9","unstructured":"Atera, SmiShing. 2024. https:\/\/www.atera.com\/glossary\/smishing\/#:~:text=An SMS message on average,leaning on mobile-based attacks. (accessed Jun. 12, 2024)."},{"key":"3532_CR10","unstructured":"Jovanovic A. 10 Facts\u2009+\u2009Stats on Smishing (SMS Phishing) in 2024, Safety Detectives, 2024. https:\/\/www.safetydetectives.com\/blog\/what-is-smishing-sms-phishing-facts\/ (accessed Aug. 08, 2024)."},{"key":"3532_CR11","unstructured":"UKWDA. New banking phishing scam by letter for Halifax bank customer details, Web Growth Consulting. https:\/\/webgrowth.co.uk\/new-banking-phishing-scam-by-letter-for-halifax-bank-customer-details\/ (accessed Dec. 12, 2023)."},{"key":"3532_CR12","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.future.2020.03.021","volume":"108","author":"S Mishra","year":"2020","unstructured":"Mishra S, Soni D. Smishing detector: a security model to detect smishing through SMS content analysis and URL behavior analysis. Futur Gener Comput Syst. 2020;108:803\u201315. https:\/\/doi.org\/10.1016\/j.future.2020.03.021.","journal-title":"Futur Gener Comput Syst"},{"issue":"12","key":"3532_CR13","doi-asserted-by":"publisher","first-page":"11117","DOI":"10.1002\/int.23035","volume":"37","author":"AK Jain","year":"2022","unstructured":"Jain AK, Gupta BB, Kaur K, Bhutani P, Alhalabi W, Almomani A. A content and URL analysis-based efficient approach to detect smishing SMS in intelligent systems. Int J Intell Syst. 2022;37(12):11117\u201341. https:\/\/doi.org\/10.1002\/int.23035.","journal-title":"Int J Intell Syst"},{"key":"3532_CR14","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.procs.2017.12.079","volume":"125","author":"AK Jain","year":"2018","unstructured":"Jain AK, Gupta BB. Rule-based Framework for detection of smishing messages in Mobile Environment. Procedia Comput Sci. 2018;125:617\u201323. https:\/\/doi.org\/10.1016\/j.procs.2017.12.079.","journal-title":"Procedia Comput Sci"},{"key":"3532_CR15","doi-asserted-by":"publisher","first-page":"24306","DOI":"10.1109\/ACCESS.2024.3364671","volume":"12","author":"M Salman","year":"2023","unstructured":"Salman M, Ikram M, Kaafar MA. Investigating Evasive Techniques in SMS Spam Filtering: A Comparative Analysis of Machine Learning Models. IEEE Access. 2023;12:24306\u201324. https:\/\/doi.org\/10.1109\/ACCESS.2024.3364671.","journal-title":"IEEE Access"},{"key":"3532_CR16","first-page":"18","volume":"1","author":"N Choudhary","year":"2017","unstructured":"Choudhary N, Jain AK. Towards filtering of SMS spam messages using machine learning based technique. Adv Inf Comput Res. 2017;1:18\u201330.","journal-title":"Adv Inf Comput Res"},{"issue":"2","key":"3532_CR17","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.icte.2022.05.009","volume":"9","author":"ON Akande","year":"2023","unstructured":"Akande ON, et al. SMSPROTECT: an automatic smishing detection mobile application. ICT Express. 2023;9(2):168\u201376. https:\/\/doi.org\/10.1016\/j.icte.2022.05.009.","journal-title":"ICT Express"},{"issue":"0","key":"3532_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19361610.2024.2372986","volume":"0","author":"PA Santosh Kumar Birthriya","year":"2024","unstructured":"Santosh Kumar Birthriya PA, Jain AK. A Comprehensive Survey of Social Engineering attacks: taxonomy of attacks, Prevention, and mitigation strategies. J Appl Secur Res. 2024;0(0):1\u201349. https:\/\/doi.org\/10.1080\/19361610.2024.2372986.","journal-title":"J Appl Secur Res"},{"issue":"12","key":"3532_CR19","doi-asserted-by":"publisher","first-page":"17823","DOI":"10.1007\/s11227-024-06148-z","volume":"80","author":"L Das","year":"2024","unstructured":"Das L, Ahuja L, Pandey A. A novel deep learning model-based optimization algorithm for text message spam detection. J Supercomput. 2024;80(12):17823\u201348. https:\/\/doi.org\/10.1007\/s11227-024-06148-z.","journal-title":"J Supercomput"},{"key":"3532_CR20","doi-asserted-by":"publisher","first-page":"4762","DOI":"10.1109\/ACCESS.2024.3349577","volume":"12","author":"JW Seo","year":"2024","unstructured":"Seo JW, et al. On-Device Smishing Classifier Resistant to Text Evasion Attack. IEEE Access. 2024;12:4762\u201379.","journal-title":"IEEE Access"},{"key":"3532_CR21","unstructured":"Almeinda SMSS, Collection, Dataset. Kaggle, 2024. https:\/\/www.kaggle.com\/datasets\/uciml\/sms-spam-collection-dataset (accessed Jan. 20, 2024)."},{"key":"3532_CR22","unstructured":"Edu S, Lee J. SMiShing dataset, Pinterest, 2024. https:\/\/www.pinterest.com.au\/seceduau\/smishing-dataset\/ (accessed Jan. 12, 2024)."},{"issue":"10","key":"3532_CR23","doi-asserted-by":"publisher","first-page":"9899","DOI":"10.1016\/j.eswa.2012.02.053","volume":"39","author":"SJ Delany","year":"2012","unstructured":"Delany SJ, Buckley M, Greene D. SMS spam filtering: methods and data. Expert Syst Appl. 2012;39(10):9899\u2013908. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.053.","journal-title":"Expert Syst Appl"},{"key":"3532_CR24","doi-asserted-by":"publisher","DOI":"10.1145\/3231884.3231895","author":"H Raj","year":"2018","unstructured":"Raj H, Weihong Y, Banbhrani SK, Dino SP. LSTM based short message service (SMS) modeling for spam classification. ACM Int Conf Proceeding Ser. 2018. https:\/\/doi.org\/10.1145\/3231884.3231895.","journal-title":"ACM Int Conf Proceeding Ser"},{"key":"3532_CR25","doi-asserted-by":"publisher","DOI":"10.1145\/2516760.2516772","author":"A Narayan","year":"2013","unstructured":"Narayan A, Saxena P. The curse of 140 characters: Evaluating the efficacy of SMS spam detection on Android. Proc ACM Conf Comput Commun Secur. 2013. https:\/\/doi.org\/10.1145\/2516760.2516772.","journal-title":"Proc ACM Conf Comput Commun Secur"},{"key":"3532_CR26","doi-asserted-by":"publisher","unstructured":"Popovac M, Karanovic M, Sladojevic S, Arsenovic M, Anderla A. Convolutional Neural Network Based SMS Spam Detection, 2018 26th Telecommun. Forum, TELFOR 2018 - Proc., pp. 1\u20134, 2018, https:\/\/doi.org\/10.1109\/TELFOR.2018.8611916","DOI":"10.1109\/TELFOR.2018.8611916"},{"key":"3532_CR27","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.future.2019.09.001","volume":"102","author":"PK Roy","year":"2020","unstructured":"Roy PK, Singh JP, Banerjee S. Deep learning to filter SMS spam. Futur Gener Comput Syst. 2020;102:524\u201333. https:\/\/doi.org\/10.1016\/j.future.2019.09.001.","journal-title":"Futur Gener Comput Syst"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03532-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03532-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03532-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T08:22:47Z","timestamp":1737966167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03532-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3532"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03532-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"29 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human or Animal"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"109"}}