Automated Emerging Cyber Threat Identification and Profiling Using Java-Based Natural Language Processing Techniques

Abstract

The continuous evolution of cyber threats poses significant challenges for proactive defense in modern digital infrastructures. Traditional detection methods often rely on signature-based or rule-driven approaches, which struggle to adapt to newly emerging and sophisticated attack patterns. To address this limitation, this paper proposes an automated cyber threat identification and profiling framework leveraging Java-based Natural Language Processing (NLP) techniques. The framework utilizes advanced NLP models to extract, analyze, and classify threat intelligence from heterogeneous sources such as cybersecurity reports, incident logs, and open-source intelligence feeds. By implementing the system in Java, interoperability, platform independence, and seamless integration with enterprise security systems are ensured. The proposed framework applies techniques such as named entity recognition (NER), topic modeling, sentiment analysis, and threat taxonomy mapping to automatically generate comprehensive threat profiles. These profiles capture attributes such as attack vectors, targeted assets, threat actors, and potential impacts, thereby assisting security analysts in proactive decision-making. Experimental evaluations conducted on benchmark cyber threat datasets demonstrate that the framework achieves high precision and recall in identifying and profiling novel threats while significantly reducing manual analysis time. This research highlights the effectiveness of combining Java-enabled NLP techniques with automated threat intelligence analysis to build scalable, efficient, and real-time solutions for emerging cyber threat management.