Abstract
Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the deficiency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.
Highlights
Phishing detection based on machine learning (ML) has received tremendous attention and interest from researchers in the cybersecurity community over the past decade
We proposed a taxonomy of phishing detection and Deep learning (DL) by dividing them into several categories
We identified the current challenges and critical issues related to DL in phishing detection and provided recommendations for future research areas
Summary
Phishing detection based on machine learning (ML) has received tremendous attention and interest from researchers in the cybersecurity community over the past decade. No study comprehensively reviews the current challenges and future trends for DL algorithms regarding phishing detection using a systematic literature review (SLR) approach. We identified the current challenges and critical issues related to DL in phishing detection and provided recommendations for future research areas. We conducted an empirical analysis of various DL architectures for phishing detection and highlighted several issues previously discussed in the literature to identify possible gaps for future research directions.
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