Accelerating a Secure Programmable Edge Network System for Smart Classroom
Watipatsa W. Nsunza, A.-Q. Ransford Tetteh, Xiaojun Hei · 2018
Internet-of-Things (IoT) applications have been challenged by vast emerging security threats. The increasing number of connected devices on an IoT network may encounter potential attacks while relaying messages over the public Internet. Security methods in traditional wireless networks are often specific to single architectures and are therefore ineffective in solving security issues that may surface on hybrid IoT networks. In this paper, we propose a software defined end-edge-cloud network architecture for smart IoT applications. We apply a deep-learning (DL) based intrusion detection system (IDS) to secure this architecture. We implement this system based on the Caffe framework and train several popular convolutional neural networks (CNNs) including LeNet, AlexNet, and ResNet-50. We evaluate the performance of this system in terms of accuracy, precision, recall, and F1score. We then investigate the FPGA-based acceleration technique to reduce the training and runtime of CNNs. We implement the FPGA acceleration based on a Xilinx KU115 board which achieves efficient performance per watt while training and deploying the IDS. For accelerating CNNs on the FPGA, we investigate a Winograd convolution engine in the Xilinx SDAccel development environment which offers automation schemes for accelerating computations. Our preliminary study may provide some insights into the experimental support for advancing IoT research and development for securing various smart applications.