AI-Driven NLP Framework for Intelligent Cyber Threat Detection and Textual Threat Analysis

With the rising complexity and volume of cyber threats, there is an urgent need for intelligent, adaptive solutions to effectively analyze unstructured textual data. Traditional signature and rule-based detection mechanisms fail to detect zero-day attacks and evolving threat patterns, especially when these threats are hidden in textual sources such as cyber threat intelligence reports, malware descriptions, vulnerability disclosures, and social media updates. This paper, therefore, proposes an artificial intelligence-based Natural Language Processing (NLP) solution for intelligent cyber threat detection and textual threat analysis. The solution proposes a hybrid CyberBERT-LSTM (Cybersecurity Bidirectional Encoder Representations from Transformers – Long Short-Term Memory) model that integrates transformation-based context features with sequential modeling to effectively capture semantic context and sequential relationships in cyber threat stories. This study evaluates the proposed CyberBERT-LSTM model in a rigorous comparison with other conventional machine learning models, including Logistic Regression, Support Vector Machines, LSTMs, and BERT. This study shows a consistent superiority of the proposed CyberBERT-LSTM model over all competitors, with accuracy = 0.98, precision = 0.97, recall rate = 0.99, and F1 measure = 0.98. Other tests performed to evaluate this proposed study include ROC AUC, precision, recall, and an F1-score of 0.98. Additional analyses using ROC-AUC, precision–recall curves, threshold sensitivity, and ablation studies further validate the robustness, reliability, and scalability of the proposed framework. The results underscore the need to integrate contextual intelligence with sequential models for effective cyber threat detection in texts, thereby firmly setting the stage for the proposed framework to serve as a viable solution to real-world challenges in cyber threat intelligence and security.