Towards a Hybrid Security Framework for Phishing Awareness Education and Defense

Peter K. K. Loh (), Aloysius Z. Y. Lee and Vivek Balachandran
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Peter K. K. Loh: Singapore Institute of Technology, 172 Ang Mo Kio Ave 8, Singapore 567739, Singapore
Aloysius Z. Y. Lee: Singapore Institute of Technology, 172 Ang Mo Kio Ave 8, Singapore 567739, Singapore
Vivek Balachandran: Singapore Institute of Technology, 172 Ang Mo Kio Ave 8, Singapore 567739, Singapore

Future Internet, 2024, vol. 16, issue 3, 1-25

Abstract: The rise in generative Artificial Intelligence (AI) has led to the development of more sophisticated phishing email attacks, as well as an increase in research on using AI to aid the detection of these advanced attacks. Successful phishing email attacks severely impact businesses, as employees are usually the vulnerable targets. Defense against such attacks, therefore, requires realizing defense along both technological and human vectors. Security hardening research work along the technological vector is few and focuses mainly on the use of machine learning and natural language processing to distinguish between machine- and human-generated text. Common existing approaches to harden security along the human vector consist of third-party organized training programmes, the content of which needs to be updated over time. There is, to date, no reported approach that provides both phishing attack detection and progressive end-user training. In this paper, we present our contribution, which includes the design and development of an integrated approach that employs AI-assisted and generative AI platforms for phishing attack detection and continuous end-user education in a hybrid security framework. This framework supports scenario-customizable and evolving user education in dealing with increasingly advanced phishing email attacks. The technological design and functional details for both platforms are presented and discussed. Performance tests showed that the phishing attack detection sub-system using the Convolutional Neural Network (CNN) deep learning model architecture achieved the best overall results: above 94% accuracy, above 95% precision, and above 94% recall.

Keywords: phishing attack; phishing email; generative AI; URL detection; phishing awareness training; machine learning; deep learning; hybrid security framework (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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