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Catching a Phish: Frontiers of Deep Learning-Based Anticipating Detection Engines

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Advances on Intelligent Informatics and Computing (IRICT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 127))

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Abstract

In recent years, cyber-security gains high attention in the light of ethical hacking and social engineering attacks like phishing that riskily overshadow the development of social networking, e commerce, and information technology. Thus, mitigation of such risks via AI techniques represents the prime research direction in academia and industry. Amongst, are the detection engines integrating diverse deep learning algorithms to anticipate changeable phishing features over time into the classification models. However, extracting the mutual features and predicting the future changes in phishing attacks still need more concrete characterization and accurate classification with fewer faults in the online engine. Upon these needs, this paper aims to present future-oriented standpoints for long-term, hybrid, cognitive, and effective phishing detection about the existing deep learning-based classification models by appraising the prior research and compiling their outstanding problems. This aim is achieved through a comparative and critical review of the prior research and then discuss the possible solutions for future phishing detection approaches.

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Author information

Authors and Affiliations

  1. Department of Networks Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq

    Hamzah Salah

  2. Department of Systems Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq

    Hiba Zuhair

Authors
  1. Hamzah Salah
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  2. Hiba Zuhair
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Corresponding author

Correspondence to Hiba Zuhair .

Editor information

Editors and Affiliations

  1. Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah, Malaysia

    Fathey Mohammed

  3. Department of Computer Science, School of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Fuad Ghaleb

Appendix

Appendix

Table A1. Comparative performance outcomes of the revisited related works
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Salah, H., Zuhair, H. (2022). Catching a Phish: Frontiers of Deep Learning-Based Anticipating Detection Engines. In: Saeed, F., Mohammed, F., Ghaleb, F. (eds) Advances on Intelligent Informatics and Computing. IRICT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-98741-1_40

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