Malicious URL Detection using Machine Learning
Shreya Samir Labhsetwar, Tushar B. Kute
Machine Learning, URL, Support Vector Machine, Decision Trees, Random Forest and k-Nearest Neighbours and logistic regression
Internet underpins a wide range of crimes, for example, spreading of Malwares and Misrepresentation of data usage. In spite of the fact that the exact inspirations driving these plans may vary, the shared factor lies in the way that clueless clients visit their locales. These visits can be driven by email, web list items or connections from other website pages. In all cases, in any case, the client is required to make some move, for example, tapping on an ideal Uniform Resource Locator (URL). In this paper, we address the identification of pernicious URL’s using various machine learning algorithms specifically Support Vector Machines, Decision Trees, Random Forest and k-Nearest Neighbours and logistic regression. Besides, we embraced an open dataset including various URLs (examples) and their corresponding labels. Specifically, Random Forest and Support Vector Machines achieve the most astounding precision. The phishing issue is tremendous and there does not exist just a single answer to limit all vulnerabilities viably, hence the systems are actualized and implemented.
Article Details
Unique Paper ID: 148580

Publication Volume & Issue: Volume 6, Issue 3

Page(s): 142 - 147
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews