Adaptive Transformer-Based Deep Learning Framework for Intelligent Dynamic Network Traffic Monitoring and Predictive Security Analytics | Journal of Engineering Strategy and Innovation | JESI
Adaptive Transformer-Based Deep Learning Framework for Intelligent Dynamic Network Traffic Monitoring and Predictive Security Analytics
Author(s) RegistrySanthosh Deep Moturi, T. Dinesh, Rajaguru Ganesan
Volume & IssueVol 1, Iss 01
Acceptance Date15 Jun 2026
Publication Date03 Jul 2026
Digital DOI Handle—
Abstract
Intelligent and adaptable security monitoring frameworks with real-time threat prediction are required due to the growing complexity of contemporary network infrastructures and the quick evolution of cyber threats. The Adaptive Transformer-Based Deep Learning Framework (ATDLF) for predictive security analytics and intelligent dynamic network traffic monitoring is presented in this research. The suggested design captures both short-term and long-term interdependence in network traffic patterns by combining an adaptive attribute refinement module with a multi-head self-attention transformer encoder. A Graph Attention Network (GAT)-based classifier is used to simulate intricate connections between network traffic constituents and boost cyber-attack classification performance in order to improve attack discrimination. Moreover, a dynamic risk prediction layer is added to predict the future risks as per the different traffic flow. To deal with idea drift and new assault patterns, the system uses continuous learning techniques and adaptive weight optimization. Experimental evaluation on benchmark intrusion detection datasets shows better performance in comparison with traditional methods. The solution proposed is a viable solution to proactive threat intelligence systems and next-generation intelligent cybersecurity monitoring.
Keywords
Deep learning (DL)intelligent networksecurity analysistraffic monitoringtransformer model
References
(15)
Nguyen, Giang, Stefan Dlugolinsky, Viet Tran, and Alvaro Lopez Garcia. "Deep learning for proactive network monitoring and security protection." ieee Access 8 (2020): 19696-19716.
Fetaji, Bekim, and Debabrata Samanta. "Hard Real-Time Deep Learning for Security-Critical Streams: An Integrative Algorithmic Framework for Cybersecurity, Scientific Analytics, and Decision Making." SN Computer Science 7, no. 5 (2026): 371.
Ghelani, Diptiben. "Deep learning and artificial intelligence framework to improve the cyber security." (2022).
Vengathattil, Sunish, and Shamnad Mohamed Shaffi. "Advanced Network Security Through Predictive Intelligence: Machine Learning Approaches for Proactive Threat Detection —An Experimental Study." Methodology 2, no. 3 (2025).
Pasupathi, Subbulakshmi, Raushan Kumar, and L. K. Pavithra. "Proactive DDoS detection: integrating packet marking, traffic analysis, and machine learning for enhanced network security." Cluster Computing 28, no. 3 (2025): 210.
Elsayed, Marwa A., and Mohammad Zulkernine. "PredictDeep: security analytics as a service for anomaly detection and prediction." IEEE Access 8 (2020): 45184- 45197.
Al-Quayed, Fatima, Zulfiqar Ahmad, and Mamoona Humayun. "A situation based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of industry 4.0." Ieee Access 12 (2024): 34800-34819.
MUTHU, SHANMUGAM, DINESH KUMAR, and DRE GURUMOORTHI. "Deep Neural Networks for Predictive Analytics and Proactive Decision-Making in Securing Critical Infrastructure."
Nagamalla, Vishwesh, and Ravi Kumar Sanapala. "Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity." International Journal of Wireless & Ad Hoc Communication 7, no. 2 (2023).
Balakrishnan, Suresh Kumar. "FRAMEWORK FOR REAL-TIME ATTACK PREDICTION AND LEGITIMATE TRAFFIC PROTECTION." Journal of Computational Analysis and Applications 33, no. 5 (2024).
Guimaraes, Lucas CB, Gabriel Antonio F. Rebello, Gustavo F. Camilo, Lucas Airam C. de Souza, and Otto Carlos MB Duarte. "A threat monitoring system for intelligent data analytics of network traffic." Annals of Telecommunications 77, no. 7 (2022): 539-554.
T. Dinesh, S. Ranushika, and C. sneka, "ResidualFormer: A Hybrid CNN-Based Encoder with Segformer-Inspired Decoder for Efficient Semantic Segmentation," Journal of Journal of Engineering Strategy and Innovation, 01(1), 1-8, 2026 8 Progressive Engineering Studies (JPES), vol. 1, no. 1, pp. 44-53, 2026.
Oseni, Ayodeji, Nour Moustafa, Gideon Creech, Nasrin Sohrabi, Andrew Strelzoff, Zahir Tari, and Igor Linkov. "An explainable deep learning framework for resilient intrusion detection in IoT-enabled transportation networks." IEEE Transactions on Intelligent Transportation Systems 24, no. 1 (2022): 1000-1014.
G. Madhu Mitha, Harshini P, Narmatha A, Ramani Sri P, and Savitha R, "Automated High-Current Short Circuit Test System for MCB," Int. J. Adv. Eng. Manag. Syst., vol. 2, no. 1, pp. 249 -256, 2026. https://doi.org/10.65379/IJAEMS/V2-I1-10Feb2026-P67
Yunhong, Guo, and Tang Guoping. "Intelligent Analysis and Dynamic Security of Network Traffic in Context of Big Data." Journal of Cyber Security and Mobility 13, no. 5 (2024): 823-841.
Adaptive Transformer-Based Deep Learning Framework for Intelligent Dynamic Network Traffic Monitoring and Predictive Security Analytics
Santhosh Deep Moturi, T. Dinesh, Rajaguru Ganesan, "Adaptive Transformer-Based Deep Learning Framework for Intelligent Dynamic Network Traffic Monitoring and Predictive Security Analytics," Journal of Engineering Strategy and Innovation (JESI), vol. 1, no. 1, pp. 1–8, 2026.
