Detecting Volumetric Attacks on loT Devices via SDN-Based Monitoring of MUD Activity

Smart environments equipped with IoT devices are increasingly under threat from an escalating number of sophisticated cyber-attacks. Current security approaches are inaccurate, expensive, or unscalable, as they require static signatures of known attacks, specialized hardware, or full packet inspection. The IETF Manufacturer Usage Description (MUD) framework aims to reduce the attack surface on an IoT device by formally defining its expected network behavior. In this paper, we use SDN to monitor compliance with the MUD behavioral profile, and develop machine learning methods to detect volumetric attacks such as DoS, reflective TCP/UDP/ICMP flooding, and ARP spoofing to IoT devices.