Phishing website detection through website traffic using Machine Learning

Phishing website detection through website traffic using Machine Learning
Author(s):
Tummala Revanth Mahindra, B. Swetha, Kappara Sri Sai Samanvita, Nalla Puneeth Krishna Reddy
Keywords:
Machine Learning Classification, Phishing Detection, Random Forest Algorithm.
Abstract
Web sites traffic largely encourage the expansion of illegal activities on the Internet and restrict the growth of Web services. Consequently, there's been a great drive for the development of methodical approaches to discourage consumers visiting these kinds of websites. Our suggestion is to use a learning-based method to divide websites into two categories: high and low. Our approach does not access the content of the websites; it just analyzes the Uniform Resource Locator. Consequently, it removes the chance of exposing users to browser-based vulnerabilities and run-time latency. With the help of learning algorithms, our system performs better in terms of generality and coverage with the blacklist service. The website URLs are divided into two classes: High: Secure websites offering standard services Low: Websites try to overwhelm consumers via advertisements or other content, such deceptive surveys.
Article Details
Unique Paper ID: 164321 Publication Volume & Issue: Volume 10, Issue 12 Page(s): 1339 - 1334
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