HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
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- Duc-Minh Ngo
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
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- Dominic Lightbody
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
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- Andriy Temko
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
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- 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
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- 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
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- Colin C. Murphy
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
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- Emanuel Popovici
- Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland
説明
<jats:p>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.</jats:p>
収録刊行物
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- Future Internet
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Future Internet 15 (1), 9-, 2022-12-26
MDPI AG
