Hybrid Digital Twin Solutions for Real-Time Threat Prevention in AI-Driven IoT Networks

Authors

  • R. Thiyagarajan Assistant Professor , Department of biomedical Engineering Shreenivasa Engineering college , Dharmapuri
  • D.Mohana Geetha Professor, Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore
  • Ahmed Mudassar Ali Professor Department of Information Technology S.A. Engineering College Chennai
  • K Sreekanth Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India-522502.

DOI:

https://doi.org/10.22399/ijcesen.2066

Keywords:

Hybrid Digital Twin, Real-Time Threat Prevention, AI-Driven IoT Networks, Anomaly Detection, Machine Learning, Predictive Analytics

Abstract

The integration of Digital Twin (DT) technology with Artificial Intelligence (AI) has shown significant promise in enhancing the security and operational efficiency of Internet of Things (IoT) networks. This paper proposes a Hybrid Digital Twin solution designed for real-time threat prevention in AI-driven IoT networks. By leveraging AI-driven decision-making processes, coupled with the real-time simulation and monitoring capabilities of Digital Twins, the proposed framework continuously analyzes IoT network behaviors and predicts potential security threats. The hybrid approach combines machine learning (ML) models with Digital Twin simulations to predict network vulnerabilities and detect anomalous behaviors at an early stage, thereby preventing security breaches before they impact the system. The architecture includes a real-time monitoring system for both physical and virtual assets, providing insights into the IoT network's current state and enabling proactive threat mitigation. Experimental results demonstrate a 15% reduction in false-positive threat detection, a 20% improvement in response time to potential threats, and a 17% increase in overall network efficiency when compared to conventional threat prevention methods. The proposed framework integrates the following components, Real-time data acquisition from IoT devices and systems, AI-based anomaly detection algorithms for threat identification, Digital Twin simulation models for continuous network status monitoring and predictive analytics. Automated response mechanisms based on AI predictions and Digital Twin assessments. The effectiveness of the proposed solution is validated through case studies and performance evaluations, highlighting its ability to enhance the security, reliability, and efficiency of AI-driven IoT networks. Future work will focus on improving the scalability of the solution, optimizing resource allocation, and extending its application to more diverse IoT environments.

References

[1] Zhang, J., Li, Y., & Zhang, X. (2020). Machine learning for IoT network security: A survey. IEEE Access, 8, 20177–20194. https://doi.org/10.1109/ACCESS.2020.2994567

[2] Gupta, R., & Gupta, A. (2020). Deep learning approaches for IoT security. International Journal of Computer Science and Network Security, 20(9), 109–118.

[3] Wang, K., et al. (2020). Digital twin models in IoT: Applications and research challenges. IEEE Internet of Things Journal, 7(10), 9047–9058. https://doi.org/10.1109/JIOT.2020.2992241

[4] Chen, M., Zhang, X., & Huang, S. (2019). Digital twin for industrial IoT: Real-time monitoring and maintenance. Computers in Industry, 109, 23–34. https://doi.org/10.1016/j.compind.2019.03.004 DOI: https://doi.org/10.1016/j.compind.2019.03.004

[5] Patel, S., Lee, M., & Du, R. Y. (2019). Hybrid digital twin approaches for IoT network optimization. Computers & Electrical Engineering, 73, 98–110. https://doi.org/10.1016/j.compeleceng.2018.11.014 DOI: https://doi.org/10.1016/j.compeleceng.2018.11.014

[6] Lin, C. Y., et al. (2020). Energy efficiency optimization in IoT systems with digital twin simulations. IEEE Transactions on Industrial Informatics, 16(5), 3641–3653. https://doi.org/10.1109/TII.2019.2941154

[7] Wang, Z., Liu, Y., & Li, J. (2021). Scalable distributed machine learning models for IoT networks. IEEE Transactions on Cloud Computing, 9(6), 1837–1849. https://doi.org/10.1109/TCC.2020.3005104

[8] Meena, A. K., & Shankar, K. A. M. (2020). AI-driven predictive analytics in IoT security. International Journal of Computational Intelligence and Applications, 19, 213–229.

[9] Gao, L., et al. (2021). Federated learning for privacy-preserving IoT security. IEEE Internet of Things Journal, 8(5), 3778–3789. https://doi.org/10.1109/JIOT.2020.3026812

[10] Sinha, R. S., & Kumar, V. (2020). AI-driven network optimization in IoT systems using digital twin. Journal of Network and Computer Applications, 108, 107–121. https://doi.org/10.1016/j.jnca.2018.11.001 DOI: https://doi.org/10.1016/j.jnca.2018.11.001

[11] Rahman, M. A., et al. (2020). AI-based resource management for IoT networks. Journal of Communication and Networks, 22(2), 151–162. https://doi.org/10.1109/JCN.2020.000015 DOI: https://doi.org/10.1109/JCN.2020.000015

[12] Hsia, T. M., Kuo, H. Y., & Wang, W. S. (2020). Smart cities with AI and digital twins: Challenges and opportunities. Journal of Urban Technology, 27(4), 1–18. https://doi.org/10.1080/10630732.2020.1731581

[13] Sood, K., Dhanaraj, R. K., Balusamy, B., Grima, S., & Uma Maheshwari, R. (Eds.). (2022). Prelims. In Big Data: A Game Changer for Insurance Industry (pp. i-xxiii). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80262-605-620221020 DOI: https://doi.org/10.1108/9781802626056

[14] Janarthanan, R., Maheshwari, R. U., Shukla, P. K., Shukla, P. K., Mirjalili, S., & Kumar, M. (2021). Intelligent detection of the PV faults based on artificial neural network and type 2 fuzzy systems. Energies, 14, 6584. https://doi.org/10.3390/en14206584 DOI: https://doi.org/10.3390/en14206584

[15] Maheshwari, R. U., Kumarganesh, S., K. V. M., S., et al. (2024). Advanced plasmonic resonance-enhanced biosensor for comprehensive real-time detection and analysis of deepfake content. Plasmonics. https://doi.org/10.1007/s11468-024-02407-0 DOI: https://doi.org/10.1007/s11468-024-02407-0

[16] Appalaraju, M., Sivaraman, A. K., Vincent, R., Ilakiyaselvan, N., Rajesh, M., & Maheshwari, U. (2022). Machine learning-based categorization of brain tumor using image processing. In R. Raje, F. Hussain, & R. J. Kannan (Eds.), Artificial Intelligence and Technologies (Vol. 806, pp. 315–324). Springer. https://doi.org/10.1007/978-981-16-6448-9_24 DOI: https://doi.org/10.1007/978-981-16-6448-9_24

[17] Maheshwari, R. U., Paulchamy, B., Pandey, B. K., et al. (2024). Enhancing sensing and imaging capabilities through surface plasmon resonance for deepfake image detection. Plasmonics. https://doi.org/10.1007/s11468-024-02492-1 DOI: https://doi.org/10.1007/s11468-024-02492-1

[18] S. S., S. S., & U. M. R. (2022). Soft computing based brain tumor categorization with machine learning techniques. In 2022 International Conference on Advanced Computing Technologies and Applications (pp. 1-9). IEEE. https://doi.org/10.1109/ICACTA54488.2022.9752880 DOI: https://doi.org/10.1109/ICACTA54488.2022.9752880

[19] Maheshwari, R. U., Paulchamy, B., Arun M., Selvaraj, V., Saranya, N. N., & Ganesh, S. S. (2024). Deepfake detection using integrate-backward-integrate logic optimization algorithm with CNN. IJEER, 12(2), 696–710. https://doi.org/10.37391/IJEER.120248 DOI: https://doi.org/10.37391/ijeer.120248

[20] Rajendran, U. M., & Paulchamy, J. (2021). Analysis and classification of gait characteristics. Iconic Research and Engineering Journals, 4(12).

[21] Paulchamy, B., Chidambaram, S., Jaya, J., & Maheshwari, R. U. (2021). Diagnosis of retinal disease using retinal blood vessel extraction. In International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020 (pp. 343-359). Springer. https://doi.org/10.1007/978-3-030-70485-0_31 DOI: https://doi.org/10.1007/978-3-030-49795-8_34

[22] Paulchamy, B., Maheshwari, R. U., Sudarvizhi, A. P. D., Anandkumar, A. P. R., & Ravi, G. (2023). Optimized feature selection techniques for classifying electrocorticography signals. In Brain-Computer Interface: Using Deep Learning Applications (pp. 255–278). Wiley. https://doi.org/10.1002/9781119857655.ch11 DOI: https://doi.org/10.1002/9781119857655.ch11

[23] Maheshwari, R. U. (2021). Encryption and decryption using image processing techniques. International Journal of Engineering Applied Sciences and Technology, 5(12). DOI: https://doi.org/10.33564/IJEAST.2021.v05i12.037

[24] Maheshwari, R. U., & Paulchamy, B. (2024). Securing online integrity: A hybrid approach to deepfake detection and removal using explainable AI and adversarial robustness training. Automatika, 65(4), 1517–1532. DOI: https://doi.org/10.1080/00051144.2024.2400640

Downloads

Published

2025-05-13

How to Cite

R. Thiyagarajan, D.Mohana Geetha, Ahmed Mudassar Ali, & K Sreekanth. (2025). Hybrid Digital Twin Solutions for Real-Time Threat Prevention in AI-Driven IoT Networks. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.2066

Issue

Vol. 11 No. 2 (2025): IJCESEN

Section

Research Article

License

Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.