Natural Language Processing–Driven Cybersecurity Threat Detection

The rapid growth of digital infrastructure has intensified the scale and sophistication of cyber threats, demanding more adaptive and intelligent detection mechanisms. Natural language processing (NLP) has emerged as a critical enabler for cybersecurity threat detection by transforming unstructured textual data—such as threat intelligence reports, security logs, phishing messages, and malware descriptions—into actionable security insights. Recent advances in transformer-based architectures and large language models have significantly improved the automated identification of malicious patterns, threat actor behaviors, indicators of compromise, and tactics, techniques, and procedures embedded within heterogeneous cyber data sources. NLP-driven systems enable semantic understanding, contextual reasoning, and cross-document knowledge extraction, overcoming the limitations of rule-based and signature-driven approaches that struggle with zero- day and polymorphic attacks. By integrating named entity recognition, relation extraction, text classification, and knowledge graph construction, modern NLP frameworks support end-to-end threat intelligence pipelines that enhance situational awareness and accelerate incident response. Moreover, the convergence of NLP with cybersecurity facilitates scalable phishing detection, malware classification, and automated threat intelligence structuring, enabling interoperability with standardized security platforms. Despite these advances, challenges remain in model robustness, explainability, domain adaptation, and resistance to adversarial manipulation. This abstract highlights the role of NLP-driven approaches in advancing cybersecurity threat detection, emphasizing their capacity to automate intelligence extraction, improve detection accuracy, and support proactive defense strategies in an evolving threat landscape.