In recent years a growing number of researchers investigated the performance of machine learning based traffic classification using statistical properties – classification techniques that do not require packet payload inspection. Such techniques assist Internet Service Providers to work within any legal or technical limitations on direct payload inspection. Potential new applications include au...

In recent years a growing number of researchers investigated the performance of machine learning based traffic classification using statistical properties – classification techniques that do not require packet payload inspection. Such techniques assist Internet Service Providers to work within any legal or technical limitations on direct payload inspection. Potential new applications include au...

It is estimated that 70 percent or more of broadband bandwidth is consumed by transmitting music, games, video and other content through P2P clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a ne...

Today, a large part of the teletraffic carried by the Internet is generated by file-sharing applications based on the peer-to-peer (P2P) paradigm. This paper considers teletraffic measurements and classification, with an emphasis on techniques which can be used to detect P2P traffic with “conventional” PC hardware, and shows some initial results of such measurements, done at the Telecommunicati...

Traffic classification is a very important mathematical and statistical tool in communications and computer networking, which is used to find average and statistical information of the traffic passing through certain pipe or hub. The results achieved from a proper deployment of a traffic analysis method provide valuable insights, including: how busy a link is, the average end-toend delays, and ...

Video surveillance has significant application prospects such as security, law enforcement, and traffic monitoring. Visual traffic surveillance using computer vision techniques can be non-invasive, cost effective, and automated. Detecting and recognizing the objects in a video is an important part of many video surveillance systems which can help in tracking of the detected objects and gatherin...

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to the problem of traffic classification in IP networks. In the case of SVMs, there are still open questions that need to be addressed before they can be generally applied to traffic classifiers. Having being designed essentially as techniques for binary classification, their genera...

The area of internet traffic classification has advanced rapidly over the last few years due to a dramatic increase in the number and variety of applications running over the internet. These applications include www, e-mail, P2P, multimedia, FTP applications, Games etc. Since traditional internet traffic classification techniques become ineffective for certain complicated applications which use...

Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classification. However, the actual performance of DPI is still unclear to the research community, since the...

Since compared with the Support Vector Machine (SVM), the Relevance Vector Machine (RVM) not only has the advantage of avoiding the overlearn which is the characteristic of the SVM, but also greatly reduces the amount of computation of the kernel function and avoids the defects of the SVM that the scarcity is not strong, the large amount of calculation as well as the kernel function must satisf...