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Defending against phishing attacks: taxonomy of methods, current issues and future directions

Author

Listed:
  • B. B. Gupta

    (National Institute of Technology Kurukshetra)

  • Nalin A. G. Arachchilage

    (The University of New South Wales)

  • Kostas E. Psannis

    (University of Macedonia)

Abstract

Internet technology is so pervasive today, for example, from online social networking to online banking, it has made people’s lives more comfortable. Due the growth of Internet technology, security threats to systems and networks are relentlessly inventive. One such a serious threat is “phishing”, in which, attackers attempt to steal the user’s credentials using fake emails or websites or both. It is true that both industry and academia are working hard to develop solutions to combat against phishing threats. It is therefore very important that organisations to pay attention to end-user awareness in phishing threat prevention. Therefore, aim of our paper is twofold. First, we will discuss the history of phishing attacks and the attackers’ motivation in details. Then, we will provide taxonomy of various types of phishing attacks. Second, we will provide taxonomy of various solutions proposed in literature to protect users from phishing based on the attacks identified in our taxonomy. Moreover, we have also discussed impact of phishing attacks in Internet of Things (IoTs). We conclude our paper discussing various issues and challenges that still exist in the literature, which are important to fight against with phishing threats.

Suggested Citation

  • B. B. Gupta & Nalin A. G. Arachchilage & Kostas E. Psannis, 2018. "Defending against phishing attacks: taxonomy of methods, current issues and future directions," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(2), pages 247-267, February.
    • Handle: RePEc:spr:telsys:v:67:y:2018:i:2:d:10.1007_s11235-017-0334-z
    DOI: 10.1007/s11235-017-0334-z
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    Citations

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    Cited by:

    1. Joakim Kävrestad & Allex Hagberg & Marcus Nohlberg & Jana Rambusch & Robert Roos & Steven Furnell, 2022. "Evaluation of Contextual and Game-Based Training for Phishing Detection," Future Internet, MDPI, vol. 14(4), pages 1-16, March.
    2. Jaime A. Teixeira da Silva & Aceil Al-Khatib & Panagiotis Tsigaris, 2020. "Spam emails in academia: issues and costs," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1171-1188, February.
    3. Robert Karamagi, 2022. "A Review of Factors Affecting the Effectiveness of Phishing," Computer and Information Science, Canadian Center of Science and Education, vol. 15(1), pages 1-20, February.
    4. Abdul Basit & Maham Zafar & Xuan Liu & Abdul Rehman Javed & Zunera Jalil & Kashif Kifayat, 2021. "A comprehensive survey of AI-enabled phishing attacks detection techniques," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 139-154, January.
    5. Aurélien Baillon & Jeroen de Bruin & Aysil Emirmahmutoglu & Evelien van de Veer & Bram van Dijk, 2019. "Informing, simulating experience, or both : A field experiment on phishing risks," Post-Print hal-04325609, HAL.
    6. Aurélien Baillon & Jeroen de Bruin & Aysil Emirmahmutoglu & Evelien van de Veer & Bram van Dijk, 2019. "Informing, simulating experience, or both: A field experiment on phishing risks," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    7. Altyeb Taha, 2021. "Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting," Mathematics, MDPI, vol. 9(21), pages 1-13, November.
    8. Dipankar Dasgupta & Zahid Akhtar & Sajib Sen, 2022. "Machine learning in cybersecurity: a comprehensive survey," The Journal of Defense Modeling and Simulation, , vol. 19(1), pages 57-106, January.

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