Hybrid key management WSN protocol to enhance network performance using ML Techniques for IoT application in cloud Environment

Abstract

As the Internet of Things (IoT) develops, Wireless Sensor Networks (WSNs) must be used to collect and transmit critical data. Ensuring the security and efficiency of these networks is paramount, given the sheer volume and sensitivity of the data they handle. This paper introduces a novel hybrid key management protocol for WSNs in IoT applications, integrating cloud services and harnessing Machine Learning (ML) techniques to enhance network performance and security. The proposed hybrid key management protocol leverages the strengths of symmetric and asymmetric essential management methods, providing a robust foundation for securing communication within the WSN. It also capitalizes on cloud-based services’ scalability and centralized management capabilities to streamline key distribution and facilitate network-wide updates. Machine Learning techniques are seamlessly integrated into the protocol, enabling predictive key distribution, anomaly detection, dynamic key management, and intelligent network load balancing. By analyzing historical data and network patterns, ML algorithms predict optimal times and locations for critical updates, reducing overhead and enhancing security. Additionally, ML-based anomaly detection empowers the protocol to identify and respond to network irregularities and potential security breaches. This framework combines centralized key management, cloud integration, and the intelligence of Machine Learning, resulting in a highly adaptable and efficient protocol for IoT-enabled WSNs. The network performance is enhanced by using exiting techniques and algorithms.