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

Duc-Minh Ngo (), Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc (), Ngoc-Thinh Tran, Colin C. Murphy () and Emanuel Popovici
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Duc-Minh Ngo: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Dominic Lightbody: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Andriy Temko: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Cuong Pham-Quoc: Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, 268 Ly Thuong Kiet St., Dist. 10, Ho Chi Minh City 740050, Vietnam
Ngoc-Thinh Tran: Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, 268 Ly Thuong Kiet St., Dist. 10, Ho Chi Minh City 740050, Vietnam
Colin C. Murphy: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
Emanuel Popovici: Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland

Future Internet, 2022, vol. 15, issue 1, 1-20

Abstract: 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.

Keywords: network security; artificial neural Networks; hardware accelerators; low power; high-performance; microcontrollers; CPU; GPU; FPGA (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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