Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), CVR College of Engineering, Vasthunagar, Mangalpalli, Ibrahimpatnam, Telangana, India.
In the era of digital transformation, the Internet of Things (IoT) has revolutionized everyday objects, and IoT gateways play a pivotal role in managing data flow within these networks. However, the dynamic and expansive nature of IoT networks poses significant cybersecurity challenges, demanding the development of adaptive security systems to protect against evolving threats. The research paper presents the development of the CoralMatrix Security framework, a novel approach to IoT cybersecurity using advanced machine learning algorithms. The framework incorporates the AdaptiNet Intelligence Model, integrating deep learning and reinforcement learning for effective real-time threat detection and response. To comprehensively evaluate the framework's performance, the study utilized the N-BaIoT dataset, facilitating a quantitative analysis that provided valuable insights into the model's capabilities. The results of the analysis showcased the CoralMatrix Security framework's robustness across various dimensions of IoT cybersecurity. Notably, the framework achieved a high detection accuracy rate of approximately 83.33%, underscoring its efficacy in identifying and responding to Cybersecurity threats in real-time. Furthermore, the research examined the framework's scalability, adaptability, resource efficiency, and robustness against diverse cyber-attack types, all quantitatively assessed to provide a comprehensive understanding of its capabilities. The paper suggests future work to optimize the framework for larger IoT networks and adapt continuously to emerging threats, aiming to expand its application across diverse IoT scenarios. The CoralMatrix Security framework, with its proposed algorithms, emerges as a promising, efficient, effective, and scalable solution for the dynamic challenges of IoT cybersecurity.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Ravikanth Motupalli, Alampally Sreedevi, Sandhya K, Geetha A and Surya Narayana Reddy V;
Writing- Original Draft Preparation: Ravikanth Motupalli, Alampally Sreedevi, Sandhya K, Geetha A and Surya Narayana Reddy V;
Visualization: Ravikanth Motupalli, Alampally Sreedevi and Sandhya K;
Investigation: Geetha A and Surya Narayana Reddy V;
Supervision: Ravikanth Motupalli, Alampally Sreedevi and Sandhya K;
Validation: Geetha A and Surya Narayana Reddy V;
Writing- Reviewing and Editing: Ravikanth Motupalli, Alampally Sreedevi, Sandhya K, Geetha A and Surya Narayana Reddy V; All authors reviewed the results and approved the final version of the manuscript.
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Surya Narayana Reddy V
Department of Computer Science and Engineering, BVRIT Hyderabad College of Engineering For Women, Hyderabad, Telangana, India.
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Cite this article
Ravikanth Motupalli, Alampally Sreedevi, Sandhya K, Geetha A and Surya Narayana Reddy V, “A Novel Machine Level Computation of Enhancing IoT Cybersecurity Logics with the Scalable and Robust Coral Matrix Security Framework”, Journal of Machine and Computing, vol.5, no.4, pp. 2643-2660, October 2025, doi: 10.53759/7669/jmc202505203.
