A fast online sequential learning accelerator for IoT network intrusion detection
Hantao Huang, Rai Suleman Khalid, Wenye Liu, Hao Yu · 2017
Deployment of IoT devices for smart buildings and homes will offer a high level of comfortability with increased energy efficiency; but can also introduce potential cyber-attacks such as network intrusions via linked IoT devices. Due to the low-power and low-latency requirement to secure IoT network, traditional software based security system is not applicable. Instead, an embedded hardware-accelerator based data analytics is more preferred for network intrusion detection. In this paper, we propose an online sequential machine learning hardware accelerator to perform realtime network intrusion detection. A single hidden layer feedforward neural network based learning algorithm is developed with a least-squares solver realized on hardware. Experimental results on a single FPGA achieve a bandwidth of 409.6 Gbps with fast yet low-power network intrusion detection based on a number of benchmarks.