HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security

• DOI: 10.3390/fi15010009
• Publication Date: 2022-12-27T12:31:56Z
• Authors: Duc-Minh Ngo; Dominic Lightbody; Andriy Temko; Cuong Pham-Quoc; Ngoc-Thinh Tran; Colin C. Murphy; Emanuel Popovici
• Keywords: low power; high-performance; artificial neural Networks; network security; 0202 electrical engineering, electronic engineering, information engineering; Information technology; 02 engineering and technology; hardware accelerators; T58.5-58.64; network security; artificial neural Networks; hardware accelerators; low power; high-performance; microcontrollers; CPU; GPU; FPGA; microcontrollers

This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT.