  IoT devices have become popular targets for various network attacks due to
their lack of industry-wide security standards. In this work, we focus on smart
home IoT device identification and defending them against Distributed Denial of
Service (DDoS) attacks. The proposed framework protects smart homes by using
VLAN-based network isolation. This architecture has two VLANs: one with
non-verified devices and the other with verified devices, both of which are
managed by the SDN controller. Lightweight stateless flow-based features,
including ICMP, TCP, and UDP protocol percentage, packet count and size, and IP
diversity ratio, are proposed for efficient feature collections. Further
analysis is performed to minimize training data to run on resource-constrained
edge devices in smart home networks. Three popular machine learning algorithms,
including K-Nearest-Neighbors, Random Forest, and Support Vector Machines, are
used to classify IoT devices and detect different types of DDoS attacks,
including TCP-SYN, UDP, and ICMP. The system's effectiveness and efficiency are
evaluated by emulating a network consisting of an Open vSwitch, Faucet SDN
controller, and several IoT device traces from two different testbeds.
