Traffic classification is a fundamental problem in achieving differentiated services for Next Generation Internet. In this work we propose a statistically based scheme to classify Internet traffic flows over a time frame, based on the application to which the packets belong. The scheme uses statistical information and thus it does not involve reading any packet headers to determine the applicat...

A key aspect of traffic classification is the early identification of individual flows which may utilise strategies such as ephemeral ports and transport later encryption to ‘hide’ on the network. This paper focuses on P2P and HTTP – the two main producers of network traffic – to determine the characteristics of their individual flows. We propose a heuristic based classification system to disti...

Internet traffic classification based on flow statistics using machine learning method has attracted great attention. To solve the drawback of the fuzzy K-Means clustering algorithm to meet the requirements of the Internet network classification, we propose Internet traffic classification based on fuzzy kernel K-Means clustering. This method overcomes the dependence of clustering algorithm on s...

1. Introduction: Recent research on Internet traffic classification has produced a flurry of proposed approaches for distinguishing types of traffic. However, rigorous comparison of the proposed algorithms remains an almost impossible task, since nearly every paper considers a different benchmark for its experimental evaluation. To shed light on scientifically grounded traffic classification re...

vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management.this paper presents novel approach of automating detecting and counting vehicles for traffic monitoring through the usage of background subtraction and morphological operators. We present adaptive background subtraction that is compatible with weather and ...

In this work we present and evaluate different automated combination techniques for traffic classification. We consider six intelligent combination algorithms applied to both traditional andmore recent traffic classification techniques using either packet content or statistical properties of flows. Preliminary results show that, when selecting complementary classifiers, some combination algorit...

Some rules in a packet classifier’s ruleset are more likely to be matched than others. Which of these rules are most likely to be matched cannot be known a priori, however. As a result, a classifier should be able to adapt itself to the traffic that it sees. This thesis explores the idea of using the traffic a packet classifier is seeing to tune that classifier’s search structure in order to im...

objectives : to estimate the disabled-adjusted life years ( dalys) of road traffic accidents in patients referred to hospitals in yazd province, central iran. methods: this cross-sectional study was conducted in yazd province during 2010. to calculate the years of life lost (yll) due to premature death and to calculate the incidence of non-fatal injuries and years lost due to disability (yld), ...

objective : to determine the epidemiological characteristics of the road traffic injuries (rtis) in guilan province, northern iran. methods: this study was a cross-sectional study which included all of rtis admitted to medical centers of guilan province (northern iran) during 2012. icd-10 was used as diagnostic criteria. demographic variables also injury circumstance and in hospital variables s...

Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be bu...