Detection of Phishing Website Using URL


Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 13 | Issue : 01 | Page : 10-15
    By

    Patel Dhruvi,

  • Dhaval Chudasama,

  1. Student, Department of Computer Engineering, Gandhinagar University, Khatraj Kalol Rd, Moti Bhoyan. Gandhinagar, Gujarat, India
  2. Assistant Professor, Department of Cyber Security, Gandhinagar University, Khatraj Kalol Rd, Moti Bhoyan, Gandhinagar, ,

Abstract

Phishing attacks are one of the greatest threats to online security, where fraud websites deceive users into giving out sensitive information. Traditional methods of detection, such as blacklists and heuristic-based systems, often fail in identifying newly created or sophisticated phishing websites. This study proposes an intelligent phishing website detection system using Convolutional Neural Networks (CNNs) in analyzing URLs and associated features. Using labeled URLs, the system employs such attributes such as URL length, domain age, HTTPS usage, and redirection patterns to train a CNN model. The potential applications include browser extensions, email filters, and cybersecurity tools. The proposed approach improves user protection against phishing attacks and mitigates the risks associated with it. This research underlines the efficiency of machine learning in dealing with dynamic and evolving cybersecurity challenges

Keywords: Phishing detection, URL analysis, machine learning, deep learning, feature extraction, decision trees, real-time detection, convolutional neural networks (CNNs)

[This article belongs to Journal Of Network security (jons)]

How to cite this article:
Patel Dhruvi, Dhaval Chudasama. Detection of Phishing Website Using URL. Journal Of Network security. 2025; 13(01):10-15.
How to cite this URL:
Patel Dhruvi, Dhaval Chudasama. Detection of Phishing Website Using URL. Journal Of Network security. 2025; 13(01):10-15. Available from: https://journals.stmjournals.com/jons/article=2025/view=196969


References

  1. Zieni R, Massari L, Calzarossa MC. Phishing or not phishing? A survey on the detection of phishing websites. IEEE Access. 2023 Feb 22; 11: 18499–519.
  2. Safi A, Singh S. A systematic literature review on phishing website detection techniques. J King Saud Univ Comput Inf Sci. 2023 Feb 1; 35(2): 590–
  3. Faheem MH, Ahmad I. Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks. Appl Sci (2076-3417). 2024 Nov 15; 14(22): 10086.
  4. Basit A, Zafar M, Liu X, Javed AR, Jalil Z, Kifayat K. A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommun Syst. 2021 Jan; 76: 139–
  5. Dutta AK. Detecting phishing websites using machine learning technique. PloS one. 2021 Oct 11; 16(10):
  6. Sharma V, Halevi T. A Survey on Phishing Website Detection Using Deep Neural Networks. In International Conference on Human-Computer Interaction. Cham: Springer Nature Switzerland; 2022 Jun 26; 684–694.
  7. Tang L, Mahmoud QH. A survey of machine learning-based solutions for phishing website detection. Mach Learn Knowl Extr. 2021 Aug 20; 3(3): 672–
  8. Nmachi WP. Phishing mitigation techniques: A literature survey. Available at SSRN 3831721. 2021 Mar 27.
  9. James J, Sandhya L, Thomas C. Detection of phishing URLs using machine learning techniques. In 2013 IEEE international conference on control communication and computing (ICCC). 2013 Dec 13; 304–309.
  10. Canfield CI, Fischhoff B, Davis A. Quantifying phishing susceptibility for detection and behavior decisions. Hum 2016 Dec; 58(8): 1158–72.
  11. Odeh A, Keshta I, Abdelfattah E. Machine learning techniques for detection of website phishing: A review for promises and challenges. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). 2021 Jan 27; 0813–0818.
  12. Alkawaz MH, Steven SJ, Hajamydeen AI. Detecting phishing website using machine learning. In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). 2020 Feb 28; 111–114.
  13. Shah A, Chudasama D. Investigating Various Approaches and Ways to Detect Cybercrime. Journal of Network Security (JoNS). 2021 Nov; 9(2): 12–
  14. Chudasama D. A Comparatively Study on Cyber Crime, Security, and Law. International Journal of Information Security and Software Engineering (IJISSE). 2022; 8(2): 15–20.
Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 26/12/2024
Accepted 04/01/2025
Published 04/02/2025
Visits: 11


Loading citations…