High Accuracy Phishing Detection Based on Convolutional Neural Networks

Date

2020-03

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this pa-per compares favourably to the state-of-the art in deep learning based phishing website detection.

Description

Keywords

Deep learning, convolutional neural networks, phishing, cyber security, cybersecurity, Machine learning, social engineering, phishing website detection

Citation

Yerima, S. Y and Alzaylaee, M. K. (2020) High Accuracy Phishing Detection Based on Convolutional Neural Networks. Third International Conference on Computer Applications & Information Security (ICCAIS 2020), Riyadh, Saudi Arabia, 19-21 March, 2020.

Rights

Research Institute

Cyber Technology Institute (CTI)