A Novel Approach on Detecting Phishing Attacks on URLS using ML Techniques

Authors

  • M. Nirmala  Assistant Professor, Department of Information Technology, Kallam Harnadha Reddy Institute of Technology, Chowdavaram, Guntur (Dt), Andhra Pradesh, India
  • I. Lakshmi Prasanna  B. Tech, Department of Information Technology, Kallam Harnadha Reddy Institute of Technology, Chowdavaram, Guntur (Dt), Andhra Pradesh, India
  • B. Meghana Sai Rajeshwari  B. Tech, Department of Information Technology, Kallam Harnadha Reddy Institute of Technology, Chowdavaram, Guntur (Dt), Andhra Pradesh, India
  • Ch. Gopi  B. Tech, Department of Information Technology, Kallam Harnadha Reddy Institute of Technology, Chowdavaram, Guntur (Dt), Andhra Pradesh, India
  • A. Vidyadhari  B. Tech, Department of Information Technology, Kallam Harnadha Reddy Institute of Technology, Chowdavaram, Guntur (Dt), Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRST52310210

Keywords:

HTML, PHISHING, HTML

Abstract

The simplest way to obtain sensitive information from unwitting users is through a phishing attack. The phishers' goal is to obtain sensitive information such as usernames, passwords, and bank account information. The proposed system is primarily concerned with detecting and preventing phishing websites. The websites are discovered using a web crawler. Phishing sites that are detected are added to the blacklist. The blacklist only contains fake websites. Web crawling is concerned with obtaining the web page's links. A phishing website can usually be identified by its URL and HTML code. The check website page alerts users to phishing websites and helps them avoid becoming victims of such attacks. This software is extremely useful in identifying and preventing the PHISHING.

References

  1. Routhu Srinivasa Rao and Syed Taqi Ali, 2015 “PhishShield: A Desktop Application to Detect Phishing Webpages through Heuristic Approach” Department of Computer Engineering, National Institute of Technology, Kurukshetra , Haryana, India, Procedia Computer Science 54,(147 – 156).
  2. Hemali Sampat, Manisha Saharkar, Ajay Pandey, Hezal Lopes, 2018” Detection of Phishing Website Using Machine Learning” Department of Computer Engineering, Universal College of Engineering, Vasai , Maharashtra, India,IRJET Volume-5 Issue-3.
  3. S. Carolin Jeeva and Elijah Blessing Raj singh. 2016 “Intelligent phishing url detection using association rule mining” Department of Computer Applications ,Karunya University, Coimbatore, India.
  4. Sa'id Abdullah Al-Saaidah, 2017 “Detecting Phishing Emails Using Machine Learning Techniques” Department of Computer Science Faculty of Information Technology Middle East University.
  5. Ram B. Basnet1, Andrew H. Sung, Quingzhong Liu, 2014 “Learning to Detect Phishing URLs” Colorado Mesa University, 1100 North Ave. Grand Jct. CO 81501, USA IJRET.
  6. Ankith Kumar jain and BB Guptha,2017 “Phishing Detection: Analysis of Visual Similarity Based Approaches” National Institute of Technology, Kurukshetra, India, Hindawi Security and Communication NetworksVolume 2017.
  7. Jian Mao, Jingdong Bian, Wenqian Tian, Shishi Zhu, Tao Wei, Aili Li and Zhenkai Liang, 2018 “Detecting Phishing Websites via Aggregation Analysis of Page Layouts” Procedia Computer Science 129 (2018) 224–230. [8] R. Kiruthiga, D. Akila,2019 “Phishing Websites Detection Using Machine Learning” (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S11.
  8. Moitrayee Chatterjee and Akbar Siami Namin, 2019“Detecting Phishing Websites through Deep Reinforcement Learning”IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). [10] M E Pratiwi ,2018 “Phishing Site Detection Analysis Using Artificial Neural Network” Journal of Physics: Conference Series1140 (2018) doi:10.1088/1742-6596/1140/1/012048.

International Journal of Scientific Research in Science and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. Nirmala, I. Lakshmi Prasanna, B. Meghana Sai Rajeshwari, Ch. Gopi, A. Vidyadhari "A Novel Approach on Detecting Phishing Attacks on URLS using ML Techniques" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.207-212, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST52310210