Flow Table Modification Using Behavioral-Based Fingerprinting Technique to Facilitate Zero Trust Identity Management and Access Control

Traditional security mechanisms and architectures are falling short, impacting both organizations and end users alike. The rapid growth and variety of Internet of Things (IoT) devices have significantly expanded the attack surface and increased network vulnerabilities. In recent years, Zero Trust (ZT) has gained traction in its attempt to solve the inherent flaws of the traditional perimeter-centric security architecture. While there have been great strides in supporting users, applications, and general-purpose computing devices (GPD), current ZT architectures lack the necessary capabilities to support IoT devices. In this paper, we present a method of authentication that profiles the network traffic and builds unique fingerprints for IoT devices. The fingerprints are a representation of the behavior of the IoT devices. We then compare the on-demand generated fingerprints of a device attempting to communicate to those in the database and update the flow tables of a software-defined networking (SDN) switch, accordingly ensuring that only devices with matching profiles can communicate on the network. We argue that the predictive nature of IoT device traffic and their limited protocol set make them a candidate for this method of authentication and access control that can satisfies the Zero Trust Architecture's identity management component.