An Efficient Model for WSN Emerging Applications Using Machine Learning

Wireless Sensor Networks (WSNs) are essential in the Internet of Things (IoT). These are widely used in modern industrial applications in which various security attacks are encountered to prevent and countermeasure the problems in WSN, such as data processing, analysis, and efficient utilization of network resources. To ensure secure data processing, techniques are proposed for protecting data privacy. Therefore, we propose a novel data-driven model by using a benchmark dataset BOT-IoT based on machine learning, which improves the performance of WSNs. Moreover, kernel of this proposed model evolved from regression based approach in use hyper-plane efficiently classified dataset into different classes. The proposed model's performance analysis has been analyzed using different metrics. The results obtained in the experiments depict that the proposed model effectively performs better than contemporary models in the state of the art.