Securing Smart Homes via Software-Defined Networking and Low-Cost Traffic Classification

Gordon, Holden; Batula, Christopher; Tushir, Bhagyashri; Dezfouli, Behnam; Liu, Yuhong

doi:10.48550/arxiv.2104.00296

Cited by 1 publication

(2 citation statements)

References 32 publications

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“…We establish a smart home testbed to collect benign and DDoS attack data by adopting various hardware and software tools [3]. Four popular IoT devices, including Google Home, Amazon Echo, Nest Camera, Ring Camera, and one Android phone, are adopted.…”

Section: B Smart Home Testing Environmentmentioning

confidence: 99%

“…Further, the DDoS traffic is joined with the benign dataset. In our previous work [3], we identified 20 seconds as the optimal polling interval when KNN achieves accuracy greater than 95% for device identification and DDoS detection using the following novel feature set. (1) Protocol percentage: for each flow data, ICMP, TCP, and UDP percentage is calculated for both benign and DDoS traffic.…”

Section: B Smart Home Testing Environmentmentioning

confidence: 99%

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An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA

Gordon¹,

Park²,

Tushir³

et al. 2021

Preprint

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With the rise in Internet of Things (IoT) devices, home network management and security are becoming complex.There is an urgent requirement to make smart home network management efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is further enhanced by offloading the computation-intensive KNN model to Field Programmable Gate Arrays (FPGA), which offers parallel processing power of GPU platforms at lower costs and higher efficiencies, and can be used to accelerate time-sensitive tasks. The proposed parallelization and implementation of KNN on FPGA are achieved by using the Vivado Design Suite from Xilinx and High-Level Synthesis (HLS). When optimized with 10-fold cross-validation, the proposed solution for KNN consistently exhibits the best performances on FPGA when compared with four alternative KNN instances (i.e., 78% faster than the parallel Bubble Sortbased implementation and 99% faster than the other three sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 milliseconds on FPGA compared to 57 seconds on a CPU platform.

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Section: B Smart Home Testing Environmentmentioning

confidence: 99%

Section: B Smart Home Testing Environmentmentioning

confidence: 99%