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WebSocket is a promising technique to build a real-time low-cost bidirectional communication channel. It supports arbitrary application-layer protocols including privately designed ones. The use of WebSocket poses a new challenge to network traffic management and security inspection. To increase visibility for WebSocket traffic, this letter proposes an automatic keyword mining approach for protocols...
Identifying applications and classifying network traffic flows according to their source applications are critical for a broad range of network activities. Such classifications can be based on information derived from packet header fields and payload content, or statistical characteristics of flows and communication patterns of hosts. However, most of present methods rely on some forms of priori knowledge...
Traditional application identification based on port numbers has become increasingly inaccurate. A more accurate alternative is to inspect the application payloads of traffic flows. The main drawback of such method is that target applications must be manually analyzed beforehand. Another alternative is to exploit the distinctive statistical properties of traffic flows and apply machine learning techniques...
Network traffic classification plays an important role in various network activities. Due to the ineffectiveness of traditional port-based and payload-based methods, recent works proposed using machine learning methods to classify flows based on statistical characteristics. In this study, we evaluate the effectiveness of machine learning techniques on the real-time traffic classification problem....
This paper is focused on a new type sneaky HTTP attack which has no obvious anomaly characteristics. A new light-weight anomaly detection scheme is introduced for large-scale Web sites whose workload is much heavier and more bursty than the general Web sites. Based on stack distance values of HTTP requests, an improved event-driven hidden semi-Markov model is applied to describe the stochastic process...
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