{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T14:05:21Z","timestamp":1777903521487,"version":"3.51.4"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030393021","type":"print"},{"value":"9783030393038","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-39303-8_17","type":"book-chapter","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T04:03:03Z","timestamp":1579838583000},"page":"219-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Catching the Phish: Detecting Phishing Attacks Using Recurrent Neural Networks (RNNs)"],"prefix":"10.1007","author":[{"given":"Luk\u00e1\u0161","family":"Halga\u0161","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Agrafiotis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason R. C.","family":"Nurse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,25]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Bahnsen, A.C., Bohorquez, E.C., Villegas, S., Vargas, J., Gonz\u00e1lez, F.A.: Classifying phishing URLs using recurrent neural networks. In: Proceedings of the APWG Symposium on Electronic Crime Research, eCrime 2017. IEEE, April 2017. \nhttps:\/\/doi.org\/10.1109\/ECRIME.2017.7945048","DOI":"10.1109\/ECRIME.2017.7945048"},{"issue":"2","key":"17_CR2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994). \nhttps:\/\/doi.org\/10.1109\/72.279181","journal-title":"IEEE Trans. Neural Netw."},{"key":"17_CR3","unstructured":"Bergholz, A., Chang, J.H., Paa\u00df, G., Reichartz, F., Strobel, S.: Improved phishing detection using model-based features. In: Proceedings of the Fifth Conference on Email and Anti-Spam, CEAS 2008, August 2008"},{"issue":"1","key":"17_CR4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3233\/JCS-2010-0371","volume":"18","author":"A Bergholz","year":"2010","unstructured":"Bergholz, A., De Beer, J., Glahn, S., Moens, M.F., Paa\u00df, G., Strobel, S.: New filtering approaches for phishing email. J. Comput. Secur. 18(1), 7\u201335 (2010). \nhttps:\/\/doi.org\/10.3233\/JCS-2010-0371\n\n. Special Issue on EU-funded ICT research on Trust and Security","journal-title":"J. Comput. Secur."},{"key":"17_CR5","unstructured":"Chandrasekaran, M., Narayanan, K., Upadhyaya, S.: Phishing email detection based on structural properties. In: Proceedings of the 9th Annual NYS Cyber Security Conference, NYSCSC 2006, June 2006"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Fette, I., Sadeh, N., Tomasic, A.: Learning to detect phishing emails. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 649\u2013656. ACM, May 2007. \nhttps:\/\/doi.org\/10.1145\/1242572.1242660","DOI":"10.1145\/1242572.1242660"},{"issue":"10","key":"17_CR7","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/72.279181","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451\u20132471 (2000). \nhttps:\/\/doi.org\/10.1109\/72.279181","journal-title":"Neural Comput."},{"key":"17_CR8","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). \nhttps:\/\/www.deeplearningbook.org"},{"issue":"8","key":"17_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). \nhttps:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Iuga, C., Nurse, J.R.C., Erola, A.: Baiting the hook: factors impacting susceptibility to phishing attacks. Hum.-Centric Comput. Inf. Sci. 6 (2016). \nhttps:\/\/doi.org\/10.1186\/s13673-016-0065-2","DOI":"10.1186\/s13673-016-0065-2"},{"key":"17_CR11","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, pp. 2342\u20132350, July 2015"},{"issue":"4","key":"17_CR12","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/SURV.2013.032213.00009","volume":"15","author":"M Khonji","year":"2013","unstructured":"Khonji, M., Iraqi, Y., Jones, A.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutor. 15(4), 2091\u20132121 (2013). \nhttps:\/\/doi.org\/10.1109\/SURV.2013.032213.00009","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"17_CR13","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. arXiv preprint (2014). \nhttps:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"2","key":"17_CR14","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s00521-013-1490-z","volume":"25","author":"RM Mohammad","year":"2014","unstructured":"Mohammad, R.M., Thabtah, F., McCluskey, L.: Predicting phishing websites based on self-structuring neural network. Neural Comput. Appl. 25(2), 443\u2013458 (2014). \nhttps:\/\/doi.org\/10.1007\/s00521-013-1490-z","journal-title":"Neural Comput. Appl."},{"key":"17_CR15","unstructured":"Nazario, J.: \nhttps:\/\/monkey.org\/~jose\/phishing\/"},{"key":"17_CR16","doi-asserted-by":"publisher","unstructured":"Nurse, J.R.C.: Cybercrime and you: how criminals attack and the human factors that they seek to exploit. In: The Oxford Handbook of Cyberpsychology. Oxford University Press, Oxford, May 2019. \nhttps:\/\/doi.org\/10.1093\/oxfordhb\/9780198812746.013.35","DOI":"10.1093\/oxfordhb\/9780198812746.013.35"},{"key":"17_CR17","unstructured":"Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. arXiv preprint (2012). \nhttps:\/\/arxiv.org\/abs\/1211.5063"},{"key":"17_CR18","unstructured":"PhishMe Inc.: 2016 enterprise phishing susceptibility and resiliency report (2016)"},{"key":"17_CR19","unstructured":"Porter, M.F.: Snowball: a language for stemming algorithms. \nhttps:\/\/snowballstem.org\/"},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-642-04117-4_23","volume-title":"Handbook of Information and Communication Security","author":"Z Ramzan","year":"2010","unstructured":"Ramzan, Z.: Phishing attacks and countermeasures. In: Stavroulakis, P., Stamp, M. (eds.) Handbook of Information and Communication Security, pp. 433\u2013448. Springer, Heidelberg (2010). \nhttps:\/\/doi.org\/10.1007\/978-3-642-04117-4_23"},{"key":"17_CR21","unstructured":"Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint (2013). \nhttps:\/\/arxiv.org\/abs\/1312.6120"},{"key":"17_CR22","unstructured":"SpamAssassin. \nhttps:\/\/spamassassin.apache.org\/old\/publiccorpus\/"},{"issue":"1","key":"17_CR23","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Toolan, F., Carthy, J.: Feature selection for spam and phishing detection. In: 2010 eCrime Researchers Summit, pp. 1\u201312. IEEE, October 2010. \nhttps:\/\/doi.org\/10.1109\/ecrime.2010.5706696","DOI":"10.1109\/ecrime.2010.5706696"},{"key":"17_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1007\/978-3-642-33167-1_47","volume-title":"Computer Security \u2013 ESORICS 2012","author":"R Verma","year":"2012","unstructured":"Verma, R., Shashidhar, N., Hossain, N.: Detecting phishing emails the natural language way. In: Foresti, S., Yung, M., Martinelli, F. (eds.) ESORICS 2012. LNCS, vol. 7459, pp. 824\u2013841. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-33167-1_47"},{"issue":"3","key":"17_CR26","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.3233\/JIFS-169423","volume":"34","author":"R Vinayakumar","year":"2018","unstructured":"Vinayakumar, R., Soman, K.P., Poornachandran, P.: Evaluating deep learning approaches to characterize and classify malicious URLs. J. Intell. Fuzzy Syst. 34(3), 1333\u20131343 (2018). \nhttps:\/\/doi.org\/10.3233\/JIFS-169423","journal-title":"J. Intell. Fuzzy Syst."},{"key":"17_CR27","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint (2014). \nhttps:\/\/arxiv.org\/abs\/arXiv:1409.2329"},{"key":"17_CR28","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-319-93554-6_36","volume-title":"Innovative Mobile and Internet Services in Ubiquitous Computing","author":"J Zhao","year":"2019","unstructured":"Zhao, J., Wang, N., Ma, Q., Cheng, Z.: Classifying malicious URLs using gated recurrent neural networks. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds.) IMIS 2018. AISC, vol. 773, pp. 385\u2013394. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-319-93554-6_36"}],"container-title":["Lecture Notes in Computer Science","Information Security Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-39303-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T04:04:56Z","timestamp":1579838696000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-39303-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030393021","9783030393038"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-39303-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"25 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Information Security Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wisa.or.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}