Intelligent System for Secure Tamper Protocol Model with IoT Blockchain Architecture for Data Mitigation

BH.V.V.S.R.K.K.Pavan; D.Venkata Satish; B.Mounica; T. Aditya Kumar

doi:10.69996/jcai.2024016

Authors

BH.V.V.S.R.K.K.Pavan Assistant Professor, Department of ECE, Bonam Venkata Chalamayya Institute of Technology and Science (Autonomous), Amalapuram, Andhra Pradesh, 533201, India. Author
D.Venkata Satish Assistant Professor, Department of ECE, Bonam Venkata Chalamayya Institute of Technology and Science (Autonomous), Amalapuram, Andhra Pradesh, 533201, India. Author
B.Mounica Assistant Professor, Department of CSE, Vignan Institute of Technology and Science Deshmukhi, Telangana, 508284, India. Author
T. Aditya Kumar Assistant Professor, Department of CSE, Keshav Memorial Institute of Technology, Hyderabad, Telangana, 500029, India. Author

DOI:

https://doi.org/10.69996/jcai.2024016

Keywords:

5G Technology, Internet of Things (IoT), Hidden Markov, Deep Learning, Routing

Abstract

In an era characterized by the proliferation of Internet of Things (IoT) devices and the critical importance of data integrity, the need for robust security mechanisms has never been greater. This paper introduces a novel "Secure Tamper Protocol Model" (STPM) integrated with an IoT-based blockchain architecture, designed to address the growing challenges of data mitigation in IoT ecosystems. This research explores the application of Symmetric Homomorphic Hidden Markov Models (SHHMMs) in the context
of anomaly detection, with a focus on %G - IoT environments. SHHMMs have shown remarkable promise in accurately identifying anomalies within diverse datasets. The study presents numerical findings indicating the model's high accuracy, precision, recall, and F1-Score, with an initial accuracy of 98.2% reaching 99.0% at Epoch 100. Comparative analysis against traditional methods like Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) models consistently highlights SHHMM's superior performance, demonstrated through Packet Delivery Ratio (PDR), Packet Loss, End-to-End Delay, and Overhead metrics. The integration of blockchain technology further enhances the practicality of SHHMM in ensuring data integrity and security. This research contributes to the advancement of anomaly detection techniques in 5G- IoT applications, offering a blend of precision and robustness.

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