Adaptive Cyber Threat Intelligence Monitoring Using Spatial–Temporal Deep Learning Models

• Author(s): Radhika S; Chinthalapuri Nagababu; R. Sakthi Vignesh; B. Abdul Fasith
• Paper ID: 1717746
• Page: 1722-1729
• Published Date: 15-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, creating major challenges for modern network security systems. Traditional cyber threat detection methods based on signatures and predefined rules often fail to identify sophisticated and evolving attacks, particularly zero-day threats. This research presents an adaptive Cyber Threat Intelligence Monitoring framework using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture for accurate and real-time threat detection. The CNN model is utilized to extract spatial characteristics from cyber threat data, including malicious traffic patterns, phishing indicators, and abnormal network behavior. The extracted features are further processed through the LSTM network to capture temporal dependencies and sequential attack patterns associated with evolving cyber intrusions. The framework follows a systematic pipeline involving dataset acquisition, preprocessing, feature extraction, threat classification, and alert generation. Continuous monitoring and automated notification mechanisms improve response efficiency and enhance network protection capabilities. Experimental evaluation using performance metrics such as accuracy, precision, recall, and F1-score demonstrates superior detection performance compared to conventional machine learning approaches. The proposed deep learning framework provides a scalable, intelligent, and proactive solution for modern cybersecurity monitoring environment

Keywords

Convolutional Neural Network (CNN), Cyber Threat Intelligence, Deep Learning, Long Short-Term Memory (LSTM), Network Security, Real-Time Threat Detection, Zero-Day Attacks

Citations

IRE Journals:
Radhika S, Chinthalapuri Nagababu, R. Sakthi Vignesh, B. Abdul Fasith "Adaptive Cyber Threat Intelligence Monitoring Using Spatial–Temporal Deep Learning Models" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 1722-1729 https://doi.org/10.64388/IREV9I11-1717746

IEEE:
Radhika S, Chinthalapuri Nagababu, R. Sakthi Vignesh, B. Abdul Fasith "Adaptive Cyber Threat Intelligence Monitoring Using Spatial–Temporal Deep Learning Models" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717746