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C-GRU:A Parallel Neural Network for Malicious Traffic Classification

机译：C-GRU：用于恶意流量分类的并行神经网络

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摘要

The network is a double-edged sword. While people enjoy the convenience brought by the development of network communication technology, various malicious behaviors hidden in the network invade users gradually. The intrusion detection system (IDS) is designed to detect the intrusion of the outside world to the system and make corresponding emergency response. Flow detection is often an important part of the IDS. However, traditional IDS are based on experts manually designing intrusion features, and feature library maintenance costs are high and new-type intrusion behaviors cannot be detected. To this end, based on the spatial-temporal characteristics of network traffic, we designed a parallel structure convolutional neural network that combines convolutional neural network (CNN) and gated recurrent unit (GRU) to classify malicious traffic. We evaluated the neural network model we designed on the public ISCX2012 dataset. The experimental results show that our model can effectively classify the type of traffic and avoid the occurrence of false alarms.
机译：网络是一把双刃剑。虽然人们享有网络通信技术的发展所带来的便利，但是网络中隐藏在网络中的各种恶意行为逐步入侵用户。入侵检测系统（IDS）旨在检测外部世界的侵入系统并进行相应的应急响应。流量检测通常是IDS的重要组成部分。但是，传统IDS基于手动设计入侵功能的专家，并且特征库维护成本高，无法检测到新型入侵行为。为此，基于网络流量的空间 - 时间特征，设计了一个并行结构卷积神经网络，将卷积神经网络（CNN）和门控复发单元（GRU）组合以对恶意流量进行分类。我们评估了我们在公共ISCX2012数据集上设计的神经网络模型。实验结果表明，我们的模型可以有效地分类交通类型，避免发生误报。

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