Machine Learning Models for Network Traffic Classification in Programmable Logic

Brendan Jacobson, Denver Conger, Bryton Petersen, Matthew Anderson, Matthew Sgambati
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引用次数: 1

Abstract

Network traffic classification via machine learning on network packet payloads has emerged as an active area of research for network security due to the high accuracy machine learning models have achieved in classifying payloads. For effective deployment as part of network security, these machine learning models must not only classify malicious packet payloads accurately, they must also identify anomalous payloads and perform inference at speeds generally faster than 10,000 packets per second to be effective. This work explores the inference speeds and accuracy of several neural network models implemented in programmable logic on various field programmable gate arrays (FPGA), including the Xilinx VC1902 and Xilinx Zynq Ultrascale+. This work also presents the design and performance of both an autoencoder and variational autoencoder programmed on the FPGA for identifying anomalous packet payloads. The performance benefits of the FPGA implementation for this type of packet payload inspection driven by machine learning are compared against graphics processing unit (GPU) inference implementations run on two state-of-the-art datacenter GPU devices, the NVIDIA V100 and A100. The model accuracy difference between the FPGA and GPU implementations was 4% or less while the Xilinx VC1902 outperformed both the NVIDIA V100 and A100 for inference speeds on all the models explored except the variational autoencoder.
可编程逻辑中网络流量分类的机器学习模型
由于机器学习模型在有效负载分类方面取得了很高的准确性,因此基于机器学习的网络流量分类已成为网络安全研究的一个活跃领域。为了作为网络安全的一部分进行有效部署,这些机器学习模型不仅必须准确地分类恶意数据包有效载荷,还必须识别异常有效载荷,并以通常快于每秒10,000个数据包的速度执行推理。这项工作探讨了在各种现场可编程门阵列(FPGA)上实现的可编程逻辑中的几种神经网络模型的推理速度和准确性,包括Xilinx VC1902和Xilinx Zynq Ultrascale+。这项工作还介绍了在FPGA上编程的自动编码器和变分自动编码器的设计和性能,用于识别异常数据包有效负载。通过机器学习驱动的这种类型的数据包有效载荷检测的FPGA实现的性能优势与在两个最先进的数据中心GPU设备(NVIDIA V100和A100)上运行的图形处理单元(GPU)推理实现进行了比较。FPGA和GPU实现之间的模型精度差异为4%或更少,而Xilinx VC1902在除变分自编码器外的所有模型上的推理速度都优于NVIDIA V100和A100。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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