CYBER THREAT INTELLIGENCE ANALYSIS OF THE DARK WEB USING A MULTI AGENT FRAMEWORK

Abstract

The rapid growth of the internet and digital technologies has led to a significant increase in cyber threats, many of which originate from the Dark Web. The Dark Web is a hidden part of the internet where cybercriminals exchange stolen data, malware, phishing kits, ransomware tools, and exploit vulnerabilities while maintaining anonymity. Monitoring these activities manually is difficult due to the large volume of unstructured data and the constantly changing nature of cyber threats. Traditional Cyber Threat Intelligence (CTI) systems often fail to provide timely and comprehensive analysis, creating the need for intelligent and automated solutions. This project proposes a Cyber Threat Intelligence Analysis of the Dark Web Using a Multi-Agent Framework, which employs multiple intelligent software agents to automatically collect, process, and analyze cyber threat information from various Dark Web sources. Each agent is responsible for specific tasks such as web crawling, data collection, preprocessing, threat classification, risk assessment, and alert generation, enabling efficient and collaborative threat intelligence analysis. The framework integrates Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) techniques to identify malicious activities, classify cyber threats, detect indicators of compromise (IOCs), recognize emerging attack patterns, and prioritize security risks. The collected data are cleaned and analyzed to extract meaningful information related to phishing campaigns, ransomware attacks, malware distribution, credential leaks, exploit discussions, and other cybercriminal activities. The multi-agent architecture enhances scalability, flexibility, fault tolerance, and processing speed by allowing multiple agents to work simultaneously on different tasks. The system generates real-time alerts, threat reports, and risk scores that help cybersecurity analysts and organizations respond quickly to potential attacks.