Malicious URL Detection Using Machine Learning

Malicious URL Detection Using Machine Learning

Ralla Anand Vardhan1, Akash Pabba2, K Veena3, Uttla Vishal Kumar4, Balaraj Yadav5

1,2,4,5 Project Students,

3 Project Guide

Department of Computer Science & Engineering

Hyderabad Institute of Technology and Management. Hyderabad

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Malicious Uniform Resource Locators (URLs), or malicious websites, are one of the most common threats to web security. They host unwanted content (spam, malware, inappropriate ads, scams, etc.) Your visit to this website may have been prompted by emails, advertisements, web searches or links from other websites. Either way, the user must click on the malicious URL. The growing prevalence of phishing, spam, and malware has led to a strong need for a reliable solution that can classify and identify malicious URLs.  In this paper, we address malicious URL detection as a binary classification problem and evaluate the performance of several known machine-learning classifiers.

Key Words: Malicious URLs, Machine Learning, Phishing, Spam, Malware, Fraud. Key Words: Malicious URLs, Machine Learning, Phishing, Spam, Malware, Fraud.