FPGA Hardware Acceleration Framework for Anomaly-based Intrusion Detection System in IoT
FPGA Hardware Acceleration Framework for Anomaly-based Intrusion Detection System in IoT
复制标题
物联网中基于异常的入侵检测系统的 FPGA 硬件加速框架
DOI：
10.1109/fpl53798.2021.00020
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
E. Popovici
中科院分区：
文献类型：
--
作者：
Duc;A. Temko;Colin C. Murphy;E. Popovici
该研究提出了一种通用的框架，用于在异构硬件上使用人工神经网络（ANN）进行实时物联网（IoT）网络入侵检测。随着交换数据量的增加，物联网网络的安全性已成为一个至关重要的问题。基于机器学习的异常入侵检测系统（IDS）由于其检测新攻击的生成能力，最近越来越受欢迎。然而，为物联网设备部署基于异常的AI辅助IDS在计算上是昂贵的。本文提出了一种IDS的分层决策方法，并在新的IoT-23数据集上进行了评估，其准确性比基于软件的方法有所提高。推理引擎在Xilinx FPGA片上系统（SoC）硬件平台上实现，可实现高性能、高准确度的攻击检测（超过99.43%）。对于最终实现的设计，人工神经网络模型在FPGA上的处理时间与xc 7z 020 clg 400设备是6.6倍和40.5倍，比GPU Quadro M2000和CPU E5-2640 2.60GHz，分别快。
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. In this paper, a hierarchical decision-making approach 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.