Holistic Cyber Threat Intelligence System with Bert for Advanced Threat Detection
N. Bala Suresh Datta · International Journal for Research in Applied Science and Engineering Technology · 2025
Cyber threats are evolving at an unprecedented rate, making traditional security measures insufficient in detecting and mitigating sophisticated attacks. This project introduces an AI-powered Cyber Threat Intelligence System that leverages machine learning, natural language processing (NLP), and automated threat analysis to enhance cybersecurity defenses. The system integrates data from multiple threat intelligence sources, such as OSINT feeds, security reports, and real-time network traffic, to identify, classify, and prioritize security threats. By employing a BERT-based NLP engine, the system can extract relevant threat entities, assign risk scores, and recommend mitigation strategies. Additionally, it incorporates Security Information and Event Management (SIEM) integration to facilitate automated security responses and real-time alerts. To improve accuracy and efficiency, the system utilizes a combination of supervised and unsupervised learning models, ensuring it adapts to new and emerging cyber threats. A key feature of the system is its automated threat prioritization mechanism, which helps security analysts focus on the most critical vulnerabilities first. The platform also supports API-based integrations with existing enterprise security solutions, enabling seamless deployment in various organizational environments. Unlike traditional signature- based detection methods, this system employs behavioral analytics to identify anomalies and zero-day threats proactively. By continuously learning from past incidents and new attack patterns, the system enhances overall cybersecurity resilience, reducing response time and improving threat intelligence capabilities.