This paper is published in Volume-8, Issue-3, 2022
Area
Machine Learning
Author
B. Naveen Kumar, S. Viswa Teja Reddy, A. Yashwanth Sai, G. Umesh Kumar, M. Srikanth
Org/Univ
Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
Pub. Date
16 May, 2022
Paper ID
V8I3-1275
Publisher
Keywords
Machine learning, Threats, Phishing, Attacks, Decision Trees, Logistic Regression

Citationsacebook

IEEE
B. Naveen Kumar, S. Viswa Teja Reddy, A. Yashwanth Sai, G. Umesh Kumar, M. Srikanth. A comparative analysis of Machine Learning Algorithms on malicious URL prediction, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
B. Naveen Kumar, S. Viswa Teja Reddy, A. Yashwanth Sai, G. Umesh Kumar, M. Srikanth (2022). A comparative analysis of Machine Learning Algorithms on malicious URL prediction. International Journal of Advance Research, Ideas and Innovations in Technology, 8(3) www.IJARIIT.com.

MLA
B. Naveen Kumar, S. Viswa Teja Reddy, A. Yashwanth Sai, G. Umesh Kumar, M. Srikanth. "A comparative analysis of Machine Learning Algorithms on malicious URL prediction." International Journal of Advance Research, Ideas and Innovations in Technology 8.3 (2022). www.IJARIIT.com.

Abstract

Phishing is a type of deception in which a person or entity poses as a legitimate user. Phishing is a technique for fooling consumers that have grown more prevalent in cyberspace. The majority of phishing texts are cryptic. Many strategies and plan has now been intended to deal with the phishing problem in the literature. There is currently no solid remedy in place to prevent such assaults. This article proposes a To detect phishing assaults, a human has to learn forecasting engine is used taking this into account. Logical regression beats the other methods in both precision and failure rate, according to the experimental investigation. With logistic regression, you can predict URLs with accuracy.