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The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for...
Network traffic classification is an essential component for network management and security systems. To address the limitations of traditional port-based and payload-based methods, recent studies have been focusing on alternative approaches. One promising direction is applying machine learning techniques to classify traffic flows based on packet and flow level statistics. In particular, previous...
We report performance evaluation of our automatic feature discovery method on the publicly available Gisette dataset: a set of 29 features discovered by our method ranks 129 among all 411 current entries on the validation set. Our approach is a greedy forward selection algorithm guided by error clusters. The algorithm finds error clusters in the current feature space, then projects one tight cluster...
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...
The LAMOST (large sky area multi-object fiber spectroscopic telescope) is one of the national key scientific projects. It will yield 10,000~20,000 spectra per observation night. Automatic spectral analysis and recognition focused on helping astronomers finding their interesting celestial objects. become desirable and necessary. In this paper an efficient data mining application based on improved Principal...
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....
With the aggravation of port economic competition, port enterprise managers pay special attention on port customer relationship management (CRM). Customer segmentation as a basis of CRM has important research meanings. In this paper, according to the characteristics of port customer data, a tree structure of data organization was obtained, reorganized the port data in 219 customer trees, introduced...
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