| Title | An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Gordon, Holden, Park, Conrad, Tushir, Bhagyashri, Liu, Yuhong, Dezfouli, Behnam |
| Conference Name | 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) |
| Keywords | Classification algorithms, Computer architecture, FPGA, HLS, IoT, KNN, Logic gates, machine learning algorithms, Network Security Architecture, parallel processing, performance evaluation, pubcrawl, resilience, Resiliency, security, smart home, Smart homes |
| Abstract | With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more 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 enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home. |
| DOI | 10.1109/LANMAN52105.2021.9478836 |
| Citation Key | gordon_efficient_2021 |
An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA