Topic

FPGA Hardware Acceleration Framework for Anomaly-based Intrusion Detection System in IoT

Abstract

This study proposes a versatile framework for real-time Internet of Things (IoT) network intrusion detection using Artificial Neural Network (ANN) on heterogeneous hardware. 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 ability to detect new attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. An efficient hierarchical decision-making for IDS is proposed and evaluated on the new IoT-23 dataset, with improved accuracy over the software-based methods. The inference engine is implemented on the Xilinx FPGA System on a Chip (SoC) hardware platform for high performance, high accuracy attack detection (more than 99.43%). For the resulting implemented design, the processing time of the ANN model on FPGA with an xc7z020clg400 device is 6.6 times and 40.5 times faster than GPU Quadro M2000 and CPU E5-2640 2.60GHz, respectively.

Seminar recored video

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About the speaker

Mr. Ngoc Duc Minh, an alumni (K2012) in major of Computer Engineering at Ho Chi Minh city University of Technology. He got the Master degree in Computer Science at the same university in 2020 and currently is a Ph.D student at University College Cork (UCC), Ireland. His interest terms are Hardware accelerators, FPGA, Network Security, Software-defined networking, Blockchain, IoTs/WSNs. See more at here.