An Integrated AI–Data Science Framework Leveraging Deep Learning–Based Natural Language Processing for Securing Intelligent Engineering Networks Against Cyber Threats

Abstract

The rapid evolution of intelligent engineering networks, driven by interconnected systems, automation, and data-centric infrastructures, has significantly increased their exposure to sophisticated cyber threats that are often dynamic, adaptive, and difficult to detect using conventional security mechanisms. This research proposes an integrated Artificial Intelligence and Data Science framework that leverages deep learning–based Natural Language Processing techniques to enhance the security and resilience of such networks. The framework is designed to analyze large volumes of structured and unstructured data, including system logs, network traffic reports, threat intelligence feeds, and textual security advisories, transforming them into actionable insights for real-time threat detection and mitigation. By employing advanced deep learning architectures, the system captures contextual patterns and semantic relationships within textual data, enabling the identification of hidden anomalies, emerging attack signatures, and coordinated intrusion attempts that may otherwise remain undetected. The integration of data science methodologies facilitates efficient data preprocessing, feature extraction, and predictive modeling, ensuring that the framework can adapt to evolving threat landscapes while maintaining high levels of accuracy and scalability. Experimental validation is conducted using benchmark cybersecurity datasets and simulated network environments to evaluate the effectiveness of the proposed model under varying attack scenarios. The results demonstrate that the framework significantly improves detection rates, reduces false positives, and enhances response time compared to traditional rule-based and signature-based systems. Furthermore, the study highlights the importance of combining linguistic analysis with network behavior monitoring to create a more holistic defense mechanism capable of addressing both known and unknown threats. The proposed approach also incorporates automated alert generation and decision support features, enabling security administrators to respond proactively and efficiently to potential breaches. By bridging the gap between AI-driven analytics and practical cybersecurity applications, this research contributes to the development of intelligent, adaptive, and robust security solutions for modern engineering networks, where reliability and data integrity are of paramount importance.