Automated Cyber Threat Intelligence Analysis using Machine Learning

Abstract

Cyber Threat Intelligence (CTI) plays a crucial role in enhancing cybersecurity by identifying, analyzing, and mitigating emerging threats. However, traditional CTI analysis methods are often manual, time-consuming, and prone to human errors, limiting their effectiveness against rapidly evolving cyber threats. In this paper, we propose an automated approach for CTI analysis using machine learning techniques. Our framework leverages natural language processing (NLP) to extract valuable threat information from unstructured threat reports, social media, and dark web sources. Additionally, we employ supervised and unsupervised learning models to classify, cluster, and predict cyber threats based on historical attack patterns. Experimental results demonstrate that our approach improves threat detection accuracy, reduces analysis time, and enhances real-time decision-making for cybersecurity professionals. The proposed system can be integrated into existing security infrastructures to strengthen proactive threat mitigation strategies.