A Survey On Leveraging Machine Learning for Phishing Attack Prediction and Detection

Year : 2025 | Volume : 12 | Issue : 03 | Page :
    By

    D Diana Julie,

  • K DineshKanna,

  • P Thirumal,

  • MP Victor Moses,

  1. Assistant Professor, Department of Artificial Intelligence and Data Science, KIT-Kalaignarkarunanidhi Institute of Technology, Kannampalayam(POST), Tamil Nadu, India
  2. Student, Department of Artificial Intelligence and Data Science, KIT-Kalaignarkarunanidhi Institute of Technology, Kannampalayam(POST), Tamil Nadu, India
  3. Student, Department of Artificial Intelligence and Data Science, KIT-Kalaignarkarunanidhi Institute of Technology, Kannampalayam(POST), Tamil Nadu, India
  4. Student, Department of Artificial Intelligence and Data Science, KIT-Kalaignarkarunanidhi Institute of Technology, Kannampalayam(POST), Tamil Nadu, India

Abstract

Phishing is one of the biggest cybersecurity threats that exploits user trust by masquerading as a legitimate site or email to steal personal and sensitive information. A state- of-the-art-phishing detection systems survey, this review showcases the evolution from traditional list-based techniques, including blacklisting and whitelisting to machine learning and deep learning models. While list-based systems cannot evolve to detect new and zero-day attacks, the ML algorithms of Decision Tree, Random Forest, Support Vector Machine, and Neural Networks have been the most widely used for URL-based phishing attacks. Hybrid models combining multiple algorithms improve detection both in terms of accuracy and robustness: because detected weaknesses found in single models can be mitigated. Recent advances in DL models, such as CNNs and LSTMs, have the ability to automatically learn complex features, which enhances detection capability. Nonetheless, evasion attacks continue to be a highly effective threat against ML classifiers. Defenses that highlight adversarial robust classification and feature learning are also reviewed. This survey focuses special attention on the continuous update and adaptation of phishing detection models for evolving attacker strategies and reliable protection in dynamic environments.

Keywords: Phishing Detection, Machine Learning, Hybrid Models, URL Features, Cybersecurity, Evasion Attacks, Random Forest, Support Vector Machine (SVM)

[This article belongs to Journal of Microelectronics and Solid State Devices ]

How to cite this article:
D Diana Julie, K DineshKanna, P Thirumal, MP Victor Moses. A Survey On Leveraging Machine Learning for Phishing Attack Prediction and Detection. Journal of Microelectronics and Solid State Devices. 2025; 12(03):-.
How to cite this URL:
D Diana Julie, K DineshKanna, P Thirumal, MP Victor Moses. A Survey On Leveraging Machine Learning for Phishing Attack Prediction and Detection. Journal of Microelectronics and Solid State Devices. 2025; 12(03):-. Available from: https://journals.stmjournals.com/jomsd/article=2025/view=234093


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 27/03/2025
Accepted 16/06/2025
Published 12/12/2025
Publication Time 260 Days
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