Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center
Seema G. Aarella, Saraju P. Mohanty, Elias Kougianos, Deepak Puthal · 2023
A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device’s behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.