A Web-Based Threat Intelligence Platform for URL Risk Analysis

Abstract

The rapid growth of cybercrime activities on the darkweb has brought organizations, researchers and cyber security professionals into real troubles.These hidden market places, credential leak repositories, money fraud hubs and anonymous communication channels often run through technologies like The Onion Router (TOR) so pinning down threats and keeping continuous monitoring becomes hard. What comes out of these covert networks is often missed by standard security monitoring tools, largely due to limited access and the chaotic nature of threat intelligence collection in real-world situations. This paper introduces a DarkWeb Monitoring Simulation platform, a web based threat intelligence concept that can analyze suspicious URLs and then create risk assessments. The proposed system combines active web scanning with passive heuristic analysis techniques to discover indicators of phishing, financial fraud, credential theft, darknet marketplaces and other malicious activities. This application is built on Python Flask and SQLite and it has user authentication, OTP verification, scan history management, and an interactive dashboard for threat visualization.In experiments, the system can sort URLs into separate tiers of risk using a weighted scoring method, and the results show this. Essentially, the platform developed is a less expensive educational alternative to commercial threat intelligence solutions and still enables practical experience with cybersecurity monitoring concepts.