A Deep-Learning-Driven Light-Weight Phishing Detection Sensor.

Wei, Bo; Hamad, Rebeen Ali; Yang, Longzhi; He, Xuan; Wang, Hao; Gao, Bin; Woo, Wai Lok

Wei, Bo; Hamad, Rebeen Ali; Yang, Longzhi; He, Xuan; Wang, Hao; Gao, Bin; Woo, Wai Lok.

Afiliación

Wei B; Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. bo.wei@northumbria.ac.uk.
Hamad RA; Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. rebeen.hamad@northumbria.ac.uk.
Yang L; Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. longzhi.yang@northumbria.ac.uk.
He X; School of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang 110169, China. hexuan@bmie.neu.edu.cn.
Wang H; Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang 110169, China. hexuan@bmie.neu.edu.cn.
Gao B; Automation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China, wanghao@cqupt.edu.cn. wanghao@cqupt.edu.cn.
Woo WL; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. bin_gao@uestc.edu.cn.

Sensors (Basel) ; 19(19)2019 Sep 30.

Article en En | MEDLINE | ID: mdl-31575038

Palabras clave

cyber security; deep learning; phishing detection

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2019 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Suiza

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