Sharif Digital Repository

Traffic modeling, traffic decomposition, and traffic engineering are some of the applications of traffic characterization that are mainly based on statistical characteristics of the network traffic. Many empirical analyses on Internet traffic traces show that the flow inter-arrival time distribution generally follows the Weibull distribution. As the scale of the network becomes larger, the Weibull distribution degrades to the Poisson distribution and when the flow arrival rate is high, it asymptotically converges to the Normal distribution. The aggregated traffic on high bandwidth links is the result of statistical multiplexing of many traffic sources, and the flow arrival rate on these... 

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user...