iFaaSBus: A Security and Privacy based Lightweight Framework for Serverless Computing using IoT and Machine Learning

Golec, M.; Ozturac, R.; Pooranian, Z.; Gill, S. S.; Buyya, R.

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes a security and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, Machine Learning (ML), and Function as a Service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token And Transport Layer Socket (TLS) protocol-based security to secure the patient's health data. iFaaSBus outperforms in terms of response time compared to non-serverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and AUC values and the K-Nearest Neighbour model gives the highest accuracy rate of 97.51 %. IEEE