Deep Learning for Secure Communication in Resource-Limited IoT Devices

Abstract

In the current era, the Internet of Things (IoT) plays a pivotal role in generating vast amounts of data, which is crucial for enhancing system performance and intelligence. This study focuses on the implementation of privacy-preserving techniques within collaborative environments, leveraging advanced learning models to safeguard sensitive information. A well-trained deep learning model is proposed, capable of processing and analyzing IoT data samples while ensuring data security. The key innovation in this model lies in offloading computational tasks to a central coordinator, which handles intensive processing without exposing critical data. Existing models often face challenges such as communication overhead and limited scalability. To address these issues, we introduce an enhanced deep-learning framework designed to minimize computational strain while improving efficiency. By shifting the majority of complex tasks to the coordinator, this model optimizes resource usage and maintains a high level of accuracy. Moreover, the proposed approach incorporates self-supervised learning to enhance its ability to analyze IoT data autonomously. In addition to its efficiency, this model is equipped with a deep learning-based cybersecurity layer, designed to detect and mitigate potential security threats in real time. By integrating cybersecurity mechanisms directly into the learning process, the model not only improves performance but also strengthens the overall security of IoT networks. Comparative results demonstrate significant improvements in both accuracy and computational efficiency, making it a viable solution for secure and intelligent IoT systems.