We study multimedia traffic classification into popular applications to assist the quality of service (QoS) support of networking technologies, including but not limited to, WiFi. For this purpose, we propose to model the multimedia traffic flow as a stochastic discrete-time Markov chain in order to take into account the strong sequentiality (i.e. the dependencies across the data instances) in the traffic flow observations. This addresses the shortcoming of the prior techniques that are based on feature extraction which is prone to losing the information of sequentiality. Also, for investigating the best application of our Markov approach to traffic classification, we introduce and test three data driven classification schemes which are all derived from the proposed model and tightly related to each other. Our first classifier has a global perspective of the traffic data via the likelihood function as a mixture of Markov components (MMC). Our second and third classifiers have local perspective based on k-nearest Markov components (kNMC) with the negative loglikelihood as a distance as well as k-nearest Markov parameters (kNMP) with the Euclidean distance. We additionally introduce to the use of researchers a rich multimedia traffic dataset consisting of four application categories, e.g., video on demand, with seven applications, e.g., YouTube. In the presented comprehensive experiments with the introduced dataset, our local Markovian approach kNMC outperforms MMC and kNMP and provides excellent classification performance, 89% accuracy at the category level and 85% accuracy at the application level and particularly over 95% accuracy for live video streaming. Thus, in test time, the nearest Markov components with the largest likelihoods yield the most discrimination power. We also observe that kNMC significantly outperforms the state-of-the-art methods (such as SVM, random forest and autoencoder) on both the introduced dataset and benchmark dataset both at the category and application levels.

我们研究流行应用程序的多媒体流量分类，以协助网络技术（包括但不限于 WiFi）的服务质量 (QoS) 支持。为此，我们建议将多媒体交通流建模为随机离散时间马尔可夫链，以考虑交通流观察中的强顺序性（即跨数据实例的依赖性）。这解决了现有基于特征提取的技术容易丢失顺序信息的缺点。此外，为了研究我们的马尔可夫方法在交通分类中的最佳应用，我们引入并测试了三个数据驱动的分类方案，它们都源自所提出的模型并且彼此紧密相关。我们的第一个分类器通过作为马尔可夫分量 (MMC) 混合的似然函数具有交通数据的全局视角。我们的第二个和第三个分类器具有基于 k-最近马尔可夫分量 (kNMC) 的局部视角，负对数似然作为距离以及 k-最近马尔可夫参数 (kNMP) 和欧几里德距离。我们还向研究人员介绍了一个丰富的多媒体流量数据集，该数据集由四个应用程序类别组成，例如视频点播，以及七个应用程序，例如 YouTube。在介绍的数据集的综合实验中，我们的局部马尔可夫方法 kNMC 优于 MMC 和 kNMP，并提供了出色的分类性能，类别级别的准确度为 89%，应用级别的准确度为 85%，尤其是直播视频流的准确度超过 95%。因此，在测试时间内，具有最大似然的最近的马尔可夫分量产生最大的辨别力。我们还观察到 kNMC 在引入的数据集和基准数据集上在类别和应用程序级别上都显着优于最先进的方法（例如 SVM、随机森林和自动编码器）。