CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity

In the current age of digital transformation, the Internet of Things (IoT) has revolutionized everyday objects, and IoT gateways play a critical role in managing the data flow within these networks. However, the dynamic and extensive nature of IoT networks presents significant cybersecurity challenges that necessitate the development of adaptive security systems to protect against evolving threats. This paper proposes the CoralMatrix Security framework, a novel approach to IoT cybersecurity that employs advanced machine learning algorithms. This framework incorporates the AdaptiNet Intelligence Model, which integrates deep learning and reinforcement learning for effective real-time threat detection and response. To comprehensively evaluate the performance of the framework, this study utilized the N-BaIoT dataset, facilitating a quantitative analysis that provided valuable insights into the model's capabilities. The results of the analysis demonstrate the robustness of the CoralMatrix Security framework across various dimensions of IoT cybersecurity. Notably, the framework achieved a high detection accuracy rate of approximately 83.33%, highlighting its effectiveness in identifying and responding to cybersecurity threats in real-time. Additionally, the research examined the framework's scalability, adaptability, resource efficiency, and robustness against diverse cyber-attack types, all of which were quantitatively assessed to provide a comprehensive understanding of its capabilities. This study 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. With its proposed algorithms, the CoralMatrix Security framework has emerged as a promising, efficient, effective, and scalable solution for the dynamic challenges of IoT Cyber Security.