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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1414122
This article is part of the Research Topic Machine Learning for CyberSecurity View all 3 articles

Heuristic Machine Learning Approaches for Identifying Phishing Threats Across Web and Email Platforms

Provisionally accepted
Ramprasath J Ramprasath J 1Krishanraj N Krishanraj N 2Alfred Daniel J Alfred Daniel J 3Chandru Vignesh Chinnappan Chandru Vignesh Chinnappan 2Dr. Jayant Giri Dr. Jayant Giri 4Hong Qin Hong Qin 5*Saurav Mallik Saurav Mallik 6
  • 1 Dr. Mahalingam College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  • 2 VIT University, Vellore, Tamil Nadu, India
  • 3 Karpagam Academy of Higher Education, Coimbatore, India
  • 4 Yeshwantrao Chavan College of Engineering (YCCE), Nagpur, Maharashtra, India
  • 5 University of Tennessee at Chattanooga, Chattanooga, Tennessee, United States
  • 6 Harvard Chan Microbiome in Public Health Center, School of Public Health, Harvard University, Boston, Massachusetts, United States

The final, formatted version of the article will be published soon.

    Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, Phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. Besides, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. The sole intention is to collect personal information through malicious program-based attempts embedded in URLs, emails, and website-based attempts. Notably, this proposed technique detects URL, email, and website-based phishing attacks, which will be beneficial and secure us from scam attempts. Subsequently, the data are pre-processed to identify phishing attacks using data cleaning, attribute selection, and attacks detected using machine learning techniques. Furthermore, the proposed techniques use heuristic-based machine learning to identify phishing attacks. Admittedly, 56 features are used to analyze URL phishing findings, and experimental results show that the proposed technique has a better accuracy of 97.2%. Above all, the proposed techniques for email phishing detection obtain a higher accuracy of 97.4%. Also, the proposed technique for website phishing detection has a better accuracy of 98.1%, and 48 features are used for analysis.

    Keywords: email, URL, website, phishing, social engineering attacks

    Received: 08 Apr 2024; Accepted: 20 Sep 2024.

    Copyright: © 2024 J, N, J, Chinnappan, Giri, Qin and Mallik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hong Qin, University of Tennessee at Chattanooga, Chattanooga, 37403, Tennessee, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.