The right choice of features to be extracted from individual or aggregated observations is an extremely critical factor for the success of modern network traffic classification approaches based on machine learning. Such activity, usually in charge of the designers of the classification scheme is strongly related to their experience and skills, and definitely characterizes the whole approach, implementation strategy as well as its performance. The main aim of this work is supporting this process by mining new and more expressive, meaningful and discriminating features from the basic ones without human intervention. For this purpose, a novel autoencoder-based deep neural network architecture is proposed where multiple autoencoders are embedded with convolutional and recurrent neural networks to elicit relevant knowledge about the relations existing among the basic features (spatial-features) and their evolution over time (temporal-features). Such knowledge, consisting in new properties that are not immediately evident and better represent the most hidden and representative traffic dynamics can be successfully exploited by machine learning-based classifiers. Different network combinations are analyzed both from a theoretical perspective, and through specific performance evaluation experiments on a real network traffic dataset. We show that the traffic classifier obtained by stacking the autoencoder with a fully-connected neural network, achieves up to a 28% improvement in average accuracy over state-of-the-art machine learning-based approaches, up to a 10% over pure convolutional and recurrent stacked neural networks, and 18% over pure feed-forward networks. It is also able to maintain high accuracy even in the presence of unbalanced training datasets.

从单个或汇总观察中提取特征的正确选择是基于机器学习的现代网络流量分类方法成功的极其关键的因素。通常由分类方案的设计者负责的这种活动与他们的经验和技能密切相关，并且绝对是整个方法，实施策略及其性能的特征。这项工作的主要目的是通过在不需要人工干预的情况下从基本特征中挖掘出新的，更具表达性，有意义和有区别的特征，从而为这一过程提供支持。以此目的，提出了一种新颖的基于自动编码器的深度神经网络架构，其中将多个自动编码器嵌入到卷积神经网络和递归神经网络中，以获取有关基本特征（空间特征）之间的关系及其随时间演变（时间特征）的相关知识。基于机器学习的分类器可以成功利用这种知识，这些知识包括不能立即显现的更好的新属性，并且可以更好地代表最隐蔽和最具代表性的交通动态。从理论角度以及通过对实际网络流量数据集的特定性能评估实验来分析不同的网络组合。我们展示了通过将自动编码器与完全连接的神经网络堆叠在一起而获得的流量分类器，与最先进的基于机器学习的方法相比，平均准确率提高了28％，与纯卷积和循环堆叠神经网络相比提高了10％，与纯前馈网络相比提高了18％。即使存在不平衡的训练数据集，它也能够保持高精度。