Home / Current Issue / Paper 1717746
Adaptive Cyber Threat Intelligence Monitoring Using Spatial–Temporal Deep Learning Models
Subject area: Science,Engineering and Technology · Area of research: Deep Learning Models
DOI: https://doi.org/10.64388/IREV9I11-1717746
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
References
[1] Admass, Wasyihun Sema, Yirga Yayeh Munaye, and Abebe Abeshu Diro. "Cyber security: State of the art, challenges and future directions." Cyber Security and Applications 2 (2024): 100031.
[2] Kaur, Jagpreet, and K. R. Ramkumar. "The recent trends in cyber security: A review." Journal of King Saud University-Computer and Information Sciences 34.8 (2022): 5766-5781.
[3] Duo, Wenli, MengChu Zhou, and Abdullah Abusorrah. "A survey of cyber attacks on cyber physical systems: Recent advances and challenges." IEEE/CAA Journal of Automatica Sinica 9.5 (2022): 784-800.
[4] Alahmadi, Amal A., et al. "DDoS attack detection in IoT-based networks using machine learning models: A survey and research directions." Electronics 12.14 (2023): 3103.
[5] Dalal, Surjeet, et al. "Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment." Journal of Cloud Computing 12.1 (2023): 1-22.
[6] Admass, Wasyihun Sema, Yirga Yayeh Munaye, and Abebe Abeshu Diro. "Cyber security: State of the art, challenges and future directions." Cyber Security and Applications 2 (2024): 100031.
[7] Kaur, Jagpreet, and K. R. Ramkumar. "The recent trends in cyber security: A review." Journal of King Saud University-Computer and Information Sciences 34.8 (2022): 5766-5781.
[8] Duo, Wenli, MengChu Zhou, and Abdullah Abusorrah. "A survey of cyber attacks on cyber physical systems: Recent advances and challenges." IEEE/CAA Journal of Automatica Sinica 9.5 (2022): 784-800.
[9] Guembe, Blessing, et al. "The emerging threat of ai-driven cyber attacks: A review." Applied Artificial Intelligence 36.1 (2022): 2037254.
[10] Sun, Nan, et al. "Cyber threat intelligence mining for proactive cybersecurity defense: A survey and new perspectives." IEEE Communications Surveys & Tutorials 25.3 (2023): 1748-1774
How to cite this paper
@article{1717746,
author = {Radhika S, Chinthalapuri Nagababu, R. Sakthi Vignesh, B. Abdul Fasith},
title = {Adaptive Cyber Threat Intelligence Monitoring Using Spatial–Temporal Deep Learning Models},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1722-1729},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717746.pdf},
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},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717746}
}