Secure Data Transmission in IoT Networks: A Machine Learning-Based Approach

This research studies the application of machine learning to enhance the security of data transport for the. of Things (IoT). Conventional encryption methods might not be adequate in Internet of Things scenarios because of the fluidity and limited resources presented by these contexts. In the course of our research, we make use of fictional Internet of Things data in order to evaluate the effectiveness of machine learning models for anomaly detection, intrusion detection, and data encryption. When it comes to resolving difficulties related to the security of the internet of things (IoT), the findings suggest that machine learning is better to traditional cryptography methodologies. The Internet of Things (IoT) security is going to be strengthened by the introduction of new strategies, dynamic security frameworks, privacy-preserving methodologies, and practical implementations in the near future. It is possible that the implementation of machine learning will make the ecosystem of the Internet of Things (IoT) more safe and efficient, which would, in turn, foster innovation across a wide range of industries.