2021/04/01 by Holden Gordon, Christopher Batula, Gordon, Holden +7 · 1 citation
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Network Security and Intrusion Detection #Software-Defined Networks and 5G
paper · pdf · doi:10.48550/arxiv.2104.00296
openalex publication_date 2021/04/01 · openalex created_date 2021/04/13 · openalex updated_date 2026/07/28
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.