Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019028" target="_blank" >RIV/62690094:18450/22:50019028 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9716113" target="_blank" >https://ieeexplore.ieee.org/document/9716113</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3151903" target="_blank" >10.1109/ACCESS.2022.3151903</a>

Result language

angličtina

Original language name

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Original language description

Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Despite decades of development and improvement, existing phishing detection techniques still suffer from the deficiency in performance accuracy and the inability to detect unknown attacks. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. In recent years, deep learning has emerged as a branch of machine learning that has become a promising solution for phishing detection. As a result, this study proposes a taxonomy of deep learning algorithms 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, phishing detection and deep learning algorithm taxonomies 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 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 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. Author

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

10

Issue of the periodical within the volume

February

Country of publishing house

US - UNITED STATES

Number of pages

35

Pages from-to

36429-36463

UT code for WoS article

000783520500001

EID of the result in the Scopus database

2-s2.0-85125341456