Implementation and Detection of Phishing Websites Using Extreme Learning Machine Based On URL

Authors

  • Omkar Ambegave  BE Scholar, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Mahesh Dhumal  BE Scholar, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Shubham Ware  BE Scholar, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Vishal Singh  BE Scholar, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Sandhyarani Shinde  BE Scholar, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Amruta Chitari  Professor, Department Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India

Keywords:

Phishing websites, Machine Learning, SVM, NB, ELM

Abstract

Phishing sites which expect to take the victims confidential data by diverting them to surf a fake website page that resembles a honest to goodness one is another type of criminal acts through the internet and its one of the especially concerns toward numerous areas including e-managing an account and retailing. Phishing site detection is truly an unpredictable and element issue including numerous components and criteria that are not stable. Proposed an intelligent model for detecting phishing web pages based on Machine Learning. Types of web pages are different in terms of their features. Hence, we must use a specific web page features set to prevent phishing attacks. We proposed a model based on Machine Learning techniques to detect phishing web pages. We have done analysis of three models of Machine Learning Algorithms and we have suggested some new rules to have efficient feature classification.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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

How to Cite

[1]
Omkar Ambegave, Mahesh Dhumal, Shubham Ware, Vishal Singh, Sandhyarani Shinde, Amruta Chitari, " Implementation and Detection of Phishing Websites Using Extreme Learning Machine Based On URL" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.259-265, May-June-2021.