Machine Learning and Enhanced Encryption for Edge Computing in IoT and Wireless Networks

Abstract

Machine Learning (ML) and better encryption methods is a key part of fixing the security and speed problems that Edge Computing causes in the Internet of Things (IoT) and Wireless Networks. The goal of this study is to improve the security of edge devices and networks so that critical data created and processed at the edge stays private and secure. Anomaly detection, threat identification, and adaptable security mechanisms depend on machine learning algorithms in a big way. These algorithms allow for proactive defenses against cyber dangers that are always changing. The proposed system used homomorphic encryption and quantum-resistant cryptography, to make data more private and secure. Even in edge devices with limited resources, these security methods keep data transfer and storage safe. The combination of machine learning and stronger encryption not only protects the IoT environment but also makes the best use of resources by changing security measures on the fly as threats change. This study adds to the development of safe and effective edge computing models, which helps IoT and wireless networks become more popular. The results can be used in many situations, from smart cities to industrial robotics. This makes sure that the advantages of edge computing can be enjoyed without putting the safety and privacy of the linked systems at risk.