{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:52:21Z","timestamp":1778356341006,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T00:00:00Z","timestamp":1569801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971093"],"award-info":[{"award-number":["61971093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771121"],"award-info":[{"award-number":["61771121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Open Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd.","award":["NRIHTOP1802"],"award-info":[{"award-number":["NRIHTOP1802"]}]},{"name":"Research and Application for key technologies of IoT Oriented to Smart Cities","award":["cstc2018jszx-cyztzx0081"],"award-info":[{"award-number":["cstc2018jszx-cyztzx0081"]}]},{"name":"Innovation and Application for Smart Test of Supply and Demand Integration","award":["cstc2018jszx-cyzd0404"],"award-info":[{"award-number":["cstc2018jszx-cyzd0404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.<\/jats:p>","DOI":"10.3390\/s19194258","type":"journal-article","created":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T13:16:41Z","timestamp":1569849401000},"page":"4258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["A Deep-Learning-Driven Light-Weight Phishing Detection Sensor"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0781-9655","authenticated-orcid":false,"given":"Bo","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebeen Ali","family":"Hamad","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2115-4909","authenticated-orcid":false,"given":"Longzhi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Sino-Dutch Biomedical &amp; Information Engineering, Northeastern University, Shenyang 110169, China"},{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Automation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-1013","authenticated-orcid":false,"given":"Bin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8698-7605","authenticated-orcid":false,"given":"Wai Lok","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"ref_1","unstructured":"(2019, July 15). Anti-Phishing Working Group (APWG). Available online: https:\/\/docs.apwg.org\/\/reports\/apwg_trends_report_q4_2018.pdf."},{"key":"ref_2","unstructured":"(2019, July 15). IC3 Annual Report 2018, Available online: https:\/\/pdf.ic3.gov\/2018_IC3Report.pdf."},{"key":"ref_3","unstructured":"(2019, July 15). Phishtank. Available online: https:\/\/www.phishtank.com\/."},{"key":"ref_4","unstructured":"(2019, July 15). Joewein. Available online: https:\/\/joewein.net\/."},{"key":"ref_5","unstructured":"(2019, July 15). Hphosts. Available online: https:\/\/www.hosts-file.net\/."},{"key":"ref_6","unstructured":"(2019, July 15). Malware Domains List. Available online: http:\/\/mirror1.malwaredomains.com."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.1109\/TMC.2016.2575828","article-title":"Detecting mobile malicious webpages in real time","volume":"16","author":"Amrutkar","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1145\/2019599.2019606","article-title":"Cantina+: A feature-rich machine learning framework for detecting phishing web sites","volume":"14","author":"Xiang","year":"2011","journal-title":"ACM Trans. Inf. Syst. Secur. (TISSEC)"},{"key":"ref_9","unstructured":"Saxe, J., and Berlin, K. (2017). eXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys. arXiv."},{"key":"ref_10","unstructured":"Le, H., Pham, Q., Sahoo, D., and Hoi, S.C. (2018). URLNet: Learning a URL representation with deep learning for malicious URL detection. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, Y., Han, W., and Le, Y. (2008, January 31). Anti-phishing based on automated individual white-list. Proceedings of the 4th ACM Workshop on Digital Identity Management, Alexandria, VA, USA.","DOI":"10.1145\/1456424.1456434"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13635-016-0034-3","article-title":"A novel approach to protect against phishing attacks at client side using auto-updated white-list","volume":"2016","author":"Jain","year":"2016","journal-title":"EURASIP J. Inf. Secur."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sharifi, M., and Siadati, S.H. (April, January 31). A phishing sites blacklist generator. Proceedings of the 2008 IEEE\/ACS International Conference on Computer Systems and Applications, Doha, Qatar.","DOI":"10.1109\/AICCSA.2008.4493625"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.eswa.2016.01.028","article-title":"New rule-based phishing detection method","volume":"53","author":"Moghimi","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.eswa.2018.09.029","article-title":"Machine learning based phishing detection from URLs","volume":"117","author":"Sahingoz","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hong, J.I., and Cranor, L.F. (2007, January 8\u201312). Cantina: A content-based approach to detecting phishing web sites. Proceedings of the 16th International Conference on World Wide Web, Banff, AB, Canada.","DOI":"10.1145\/1242572.1242659"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, J., Chen, J., Choo, K.K.R., Liu, C., Liu, K., Yu, M., and Wang, Y. (2017). A deep learning based online malicious URL and DNS detection scheme. International Conference on Security and Privacy in Communication Systems, Springer.","DOI":"10.1007\/978-3-319-78813-5_22"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15196","DOI":"10.1109\/ACCESS.2019.2892066","article-title":"Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"56329","DOI":"10.1109\/ACCESS.2019.2913705","article-title":"Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism","volume":"7","author":"Fang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (26, January June). Decaf: A deep convolutional activation feature for generic visual recognition. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","unstructured":"(2019, February 15). Alexa Top Sites. Available online: https:\/\/www.alexa.com\/topsites."},{"key":"ref_26","unstructured":"(2019, February 15). Malwaredomains. Available online: https:\/\/www.malwaredomains.com\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4258\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:19Z","timestamp":1760189179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,30]]},"references-count":26,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194258"],"URL":"https:\/\/doi.org\/10.3390\/s19194258","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,30]]}}}