Nowadays attacks on computer networks continue to advance at a rate outpacing cyber defenders’ ability to write new attack signatures. This paper illustrates a deep learning methodology for the binary classification of the network traffic. The basic idea is to represent network flows as 2D images and use this imagery representation of the network traffic to train a Generative Adversarial Network (GAN) and a Convolutional Neural Network (CNN). The GAN is trained to produce new images of unforeseen network attacks by augmenting the training data used to learn a CNN-based intrusion detection model. The advantage is that the 2D data mapping technique used builds images of the network flows, which allow us to take advantage of deep learning architectures with convolution layers. In addition, the GAN-based data augmentation allows us to deal with the possible imbalance of malicious traffic that is commonly rarer than the normal traffic in the network traffic. Specifically, it is used to simulate unforeseen attacks to train a robust intrusion detection model. The proposed methodology leads to better predictive accuracy when compared to competitive intrusion detection architectures on four benchmark datasets.

如今，对计算机网络的攻击继续以超过网络防御者编写新攻击签名的能力的速度发展。本文说明了用于网络流量的二进制分类的深度学习方法。基本思想是将网络流表示为2D图像，并使用网络流量的此图像表示来训练生成对抗网络（GAN）和卷积神经网络（CNN）。通过增加用于学习基于CNN的入侵检测模型的训练数据，GAN被训练为生成不可预见的网络攻击的新图像。优势在于所使用的2D数据映射技术可构建网络流的图像，这使我们能够利用具有卷积层的深度学习架构。此外，基于GAN的数据增强功能使我们能够处理可能比网络流量中的正常流量少的恶意流量失衡。具体来说，它用于模拟无法预料的攻击，以训练健壮的入侵检测模型。与四个基准数据集上的竞争入侵检测体系结构相比，所提出的方法具有更好的预测准确性。