The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

中文翻译：

---

基于 Hurst 指数分析的卷积神经网络远程相关流量分类

该论文研究了神经网络根据 Hurst 指数表示的自相似性对互联网流量数据进行分类的能力。分数高斯噪声用于生成用于模拟真实数据的合成数据。表明训练模型能够对从帕累托分布获得的合成数据和实际交通数据进行分类。我们展示了成本函数的不同优化器和神经网络中不同数量的卷积层的训练结果。