International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid growth of cybercrime and dark web activities has made threat monitoring an essential task for cybersecurity analysts. Many organizations struggle to identify potential cyber threats quickly due to the large volume of unstructured textual data generated across various online sources. This project introduces ThreatSpeak, a machine learning-based system designed to analyze and classify cyber threat information from textual data. The system allows users to upload threat-related text files, which are automatically preprocessed and analyzed using natural language processing techniques. A trained machine learning model categorizes the content into relevant cyber threat types such as phishing, malware, or data breaches. In addition to classification, the system extracts threat indicators, identifies potential targeted assets, and generates a concise analytical summary to assist cybersecurity professionals in understanding the threat context. The application is implemented using Python, Streamlit, and Scikit-learn, providing a simple web interface for threat analysis. ThreatSpeak demonstrates how machine learning can support cybersecurity intelligence by improving the speed and accuracy of threat identification from textual sources.

Keywords: Cybersecurity, Threat Intelligence, Machine Learning, Natural Language Processing, Dark Web Monitoring

Downloads: | DOI: 10.17148/IJARCCE.2026.15248