Secure and Efficient Data Transmission in IoT Networks using Machine Learning-Based Encryption

Thesis Abstract

Abstract


The proliferation of IoT networks has led to an increased need for secure and efficient data transmission. This research focuses on addressing this need through the utilization of machine learning-based encryption techniques. The study begins with a comprehensive review of IoT networks, security challenges, and existing encryption approaches. A detailed methodology for security requirements analysis, selection of machine learning-based encryption algorithms, and protocol design is presented. The implementation phase involves the development of a machine learning-based encryption model, integration of encryption protocols in IoT networks, and performance evaluation. The results are analyzed, compared with conventional encryption methods, and visualized to demonstrate the enhancements achieved in secure and efficient data transmission. The thesis concludes with a summary of research contributions, implications, and recommendations for future work in the field of secure and efficient data transmission in IoT networks using machine learning-based encryption. This research is expected to provide valuable insights and practical solutions for addressing security concerns in IoT data transmission.