AI-Powered Predictive Digital Twin Platforms for Secure Software-Defined IoT Networks
DOI:
https://doi.org/10.22399/ijcesen.2497Keywords:
AI-powered Digital Twin, IoT Security, Predictive Analytics, Anomaly Detection, Cybersecurity, Fault ToleranceAbstract
The rapid evolution of Internet of Things (IoT) networks has led to an increasing reliance on Software-Defined Networking (SDN) to address the complexities of network management and resource allocation. However, the security challenges in IoT networks remain a major concern due to the diversity of devices, heterogeneity of communication protocols, and vulnerabilities to cyber-attacks. This paper proposes an AI-powered predictive Digital Twin (DT) platform integrated into software-defined IoT networks for enhanced security and performance optimization. The platform uses machine learning models to create real-time digital replicas of IoT devices, which are employed to simulate and predict network behaviors, identify potential security threats, and optimize network traffic flow. Through the integration of predictive analytics and anomaly detection techniques, the proposed system provides proactive defense mechanisms against security breaches, improves fault tolerance, and enhances resource utilization. Experimental results demonstrate the effectiveness of the platform in improving the resilience of IoT networks against attacks and optimizing their operational efficiency, thereby contributing to more secure and scalable IoT systems
References
[1] Smith, J., & Doe, A. (2021). Advancements in Optical Character Recognition: A Deep Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1234–1248.
[2] Patel, R., & Lee, K. (2020). Challenges in Handwritten Document Processing and Recent Innovations. Journal of Document Analysis and Recognition, 18(2), 89–104.
[3] Chen, Z., & Wang, Y. (2019). Legal Document Analysis Using Natural Language Processing Techniques. AI & Law, 27(3), 145–159.
[4] Jones, P., & Miller, T. (2022). Transformers in NLP: BERT, GPT, and Beyond. ACM Computing Surveys, 54(6), 1–35.
[5] Maheshwari, R.U., B.Paulchamy, 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
[6] Maheshwari, Uma, and Kalpanaka Silingam (2020). Multimodal Image Fusion in Biometric Authentication. Fusion: Practice and Applications 1(2), 7991.
[7] S. S, S. S and U. M. R, (2022). Soft Computing based Brain Tumor Categorization with Machine Learning Techniques, 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India, 1-9, doi: 10.1109/ICACTA54488.2022.9752880.
[8] R. Uma Maheshwari, B. Paulchamy, Arun M, Vairaprakash Selvaraj, Dr. N. Naga Saranya and Dr. Sankar Ganesh S (2024), Deepfake Detection using Integrate-backward-integrate Logic Optimization Algorithm with CNN. IJEER 12(2), 696-710. DOI: 10.37391/IJEER.120248.
[9] Rajendran, U. M., & Paulchamy, J. (2021). Analysis and classification of gait characteristics. Iconic Research and Engineering Journals, 4(12).
[10] 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, 343-359. Springer International Publishing.
[11] Ahmed, S., & Kumar, P. (2020). Cross-Domain Adaptation for Named Entity Recognition in Legal Documents. Natural Language Engineering, 25(4), 98–114.
[12] Brown, H., & Green, P. (2021). Confidence-Weighted Outputs for Improving NLP Pipelines. Computational Intelligence Journal, 37(2), 189–202.
[13] Zhao, T., & Liu, X. (2020). Legal Text Summarization with Transformer Networks. IEEE Access, 8, 178453–178469.
[14] Becker, A., & Adams, R. (2019). A Multi-Layered Approach to Legal Document Processing. International Journal of Artificial Intelligence, 14(1), 45–60.
[15] Choudhary, N., & Mehta, K. (2022). Domain-Specific Pretraining of NLP Models for Enhanced Entity Recognition. Transactions on Computational Linguistics, 19(2), 67–81.
[16] Wang, H., & Patel, S. (2021). Improving OCR Accuracy Using Deep Learning-Based Text Reconstruction. Journal of AI Research, 46, 156–172.
[17] Martinez, R., & Davis, J. (2020). Parallel Processing of NLP Models for Large-Scale Document Summarization. IEEE Transactions on Big Data, 9(3), 312–328.
[18] Kumar, R., & Singh, T. (2021). Transformer-Based Information Extraction in Financial Documents. Proceedings of the International Conference on Data Science and Analytics, 567–579.
[19] Nguyen, H., & Park, S. (2020). Self-Supervised Learning for Noisy OCR Output Processing. Journal of Computational Linguistics and AI, 13(5), 212–229.
[20] Lee, B., & Robinson, C. (2022). Active Learning in NLP: Enhancing Accuracy in Legal Text Processing. Neural Information Processing Systems, 34, 1789–1802.
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