Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection

被引：15

作者：

Adebowale, M. A. ^{[1
]}

Lwin, K. T. ^{[2
]}

Hossain, M. A. ^{[2
]}

机构：

[1] Anglia Ruskin Univ, Sch Comp & Informat Sci, Chelmsford, England

[2] Teesside Univ, Sch Comp & Digital Technol, Middlesbrough, Cleveland, England

来源：

2019 13TH INTERNATIONAL CONFERENCE ON SOFTWARE, KNOWLEDGE, INFORMATION MANAGEMENT AND APPLICATIONS (SKIMA) | 2019年

关键词：

Phishing detection; Cybercrime; Deep learning (DL); Convolutional Neural Network (CNN); Long Short-Term Memory (LSTM); Big data; Universal Resource Locator (URL);

D O I：

10.1109/skima47702.2019.8982427

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Phishers sometimes exploit users' trust of a known website's appearance by using a similar page that looks like the legitimate site. In recent times, researchers have tried to identify and classify the issues that can contribute to the detection of phishing websites. This study focuses on design and development of a deep learning based phishing detection solution that leverages the Universal Resource Locator and website content such as images and frame elements. A Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) algorithm were used to build a classification model. The experimental results showed that the proposed model achieved an accuracy rate of 93.28%.

引用

页数：8

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