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

Adebowale, Moruf A. and Lwin, Khin and Hossain, Mohammed (2020) Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection. In: 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Ulkulhas, Maldives.

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Official URL: http://dx.doi.org/10.1109/SKIMA47702.2019.8982427

Abstract

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%.

Item Type: Conference or Workshop Item (Paper)
Keywords: phishing detection, cybercrime, deep learning
Faculty: Faculty of Science & Engineering
Depositing User: Ian Walker
Date Deposited: 29 Jan 2020 12:11
Last Modified: 09 Sep 2021 18:53
URI: https://arro.anglia.ac.uk/id/eprint/705137

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