A Review on Phishing Website Detection Using Machine Learning Approach

Authors

  • Nikita Pawar  ME Student, Computer Science and Engineering, Sipna COET, Amravati, Maharashtra, India
  • Dr. P. A. Tijare  Professor, Computer Science and Engineering, Sipna COET, Amravati, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2390227

Keywords:

Phishing website, Phishing URL Detection, Machine learning.

Abstract

Phishing attacks are a rapidly growing threat in the cyber world. Every person is truly dependent on the internet. Everyone is doing online purchasing and online activities like online banking, online booking, and online billing on the internet. Phishing is a type of threat on a website and phishing is the illegal use of real information on the website information such as login id, password, information of credit card. Phishers use websites that visually and semantically resemble real websites. How can someone tell the difference between a website that is a phishing website and a real website? The identification of a website as a phishing website depends on several factors such as URL length, including the special letter '@', double slash redirect, and the existence of subdomains. Although the above factors are present on the website, no one can claim that the website is a phishing website, it can also be an original website. For solving this type of problem we can use the machine learning algorithm. The review creates phishing attack alerts, detects the attack, and motivate readers to use phishing prevention. With a large number of phishing emails or messages coming today, it is for businesses or individuals impossible to find them.

References

  1. Arathi Krishna V, Anusree A, Blessy Jose, Karthika Anilkumar, Ojus Thomas Lee, “Phishing Detection using Machine Learning based URL Analysis: A Survey”, International Journal of Engineering Research & Technology (IJERT), Volume 9, Issue 13, pp.156-160, 2021.
  2. Ammar Odeh, Ismail Keshta, Eman Abdelfattah, “Machine Learning Techniques for Detection of Website Phishing: A Review for Promises and Challenges”, 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), DOI:10.1109/CCWC51732.2021.937599, 2021.
  3. Iman Akour, Noha Alnazzawi, Ahmad Aburayya, Raghad Alfaisal, Said A. Salloum, “Using Classical Machine Learning For Phishing Websites Detection From URLS”, Journal of Management Information and Decision Sciences, Volume 24, Special Issue 6, 2021.
  4. Jitendra Kumar, Balaji Rajendran, A. Santhanavijayan, B. Janet, Bindhumadhava BS, “Phishing Website Classification and Detection Using Machine Learning”, 2020 International Conference on Computer Communication and Informatics (ICCCI -2020), 2020.
  5. Korkmaz, Ozgur Koray Sahingoz, Banu Diri, “Detection of Phishing Websites by Using Machine Learning- Based URL Analysis”, 11nth International Conference on Computing Communication and Networking Technologies (ICCCNT), 2020.
  6. Mohammad Nazmul Alam, Dhiman Sarma et al., “Phishing attacks detection using machine learning approach,” 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020.
  7. Ilker Kara, Murathan Ok,Ahmet Ozaday, “Characteristics of Understanding URLs and Domain Names Features: The Detection of Phishing Websites With Machine Learning Methods”,IEEE Access, Volume 10, 2022, DOI:10.1109/ACCESS.2022.3223111, pp.124420-124428, 2022.
  8. Malak Aljabri, Hanas S. Altamimi, Shahd A. Albelali, Maimunah Al-Harbi, Haya T. Alhuraib, Najd K. Alotaibi, Amal A. Alahmadi,Fahd Alhaidari, Rami Mustafa
  9. A. Mohammad, Khaled Salah, “Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions”, IEEE Access, Volume 10, 2022, DOI:10.1109/ACCESS.2022.3222307, pp. 121395-121417, 2022.

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Published

2023-04-30

Issue

Section

Research Articles

How to Cite

[1]
Nikita Pawar, Dr. P. A. Tijare, " A Review on Phishing Website Detection Using Machine Learning Approach, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.267-272, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390227