A Machine Learning Driven Threat Intelligence System for Malicious URL Detection

Abstract

Malicious websites predominantly promote the growth of criminal activities over the Internet restraining the development of web services. Furthermore, we see different types of devices being equipped with WiFi capabilities, that allow web traffic to pass through the device’s data systems with ease. The proposed framework in the present study analyzes the Uniform Resource Locator (URL) through which malicious users can gain access to the content of the websites. It thus eliminates issues of run-time latency and possibilities of users being subjected to browser oriented vulnerabilities. The primary objective of this paper is to detect malicious links on the web using a machine learning classification technique that would help users defend against cyber-crime attacks and related threats of the real world. This may be helpful in the newly expanding Intelligent Infrastructures, where we see more data availability almost daily. The embedding of malicious URLs is a predominant web threat faced by the Internet community in the present day and age. Attackers falsely claim of being a trustworthy entity and lure users to click on compromised links to extract confidential information, victimizing them towards identity theft. The present work explores the various ways of detecting malicious links from the host-based and lexical features of the URL in order to protect users from being subjected to identity theft attacks.