Classifying network traffic in the big data era

With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.

2Loud?: Community mapping of exposure to traffic noise with mobile phones

Abstract: Despite ample medical evidence of the adverse impacts of traffic noise on health, most policies for traffic noise management are arbitrary or incomplete, resulting in serious social and economic impacts. Surprisingly, there is limited information about citizen’s exposure to traffic noise worldwide. This paper presents the 2Loud? mobile phone application, developed and tested as a methodology to monitor, assess and map the level of exposure to traffic noise of citizens with focus on the night period and indoor locations, since sleep disturbance is one of the major triggers for ill health related to traffic noise. Based on a community participation experiment using the 2Loud? mobile phone application in a region close to freeways in Australia, the results of this research indicates a good level of accuracy for the noise monitoring by mobile phones and also demonstrates significant levels of indoor night exposure to traffic noise in the study area. The proposed methodology, through the data produced and the participatory process involved, can potentially assist in planning and management towards healthier urban environments.

On addressing the imbalance problem : A correlated KNN approach for network traffic classification

With the arrival of big data era, the Internet traffic is growing exponentially. A wide variety of applications arise on the Internet and traffic classification is introduced to help people manage the massive applications on the Internet for security monitoring and quality of service purposes. A large number of Machine Learning (ML) algorithms are introduced to deal with traffic classification. A significant challenge to the classification performance comes from imbalanced distribution of data in traffic classification system. In this paper, we proposed an Optimised Distance-based Nearest Neighbor (ODNN), which has the capability of improving the classification performance of imbalanced traffic data. We analyzed the proposed ODNN approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments were implemented on the real-world traffic dataset. The results show that the performance of “small classes” can be improved significantly even only with small number of training data and the performance of “large classes” remains stable.

Differential organotin sensitivity in two Leucozonia species from a ship traffic area in southeastern Brazil

Tributyltin exposure is the primary cause of imposex development in gastropods, making them excellent bioindicators. The present research represents the first examination of butyltin (BT) contamination in surface sediments associated with imposex in Leucozonia nassa and L. ocellata in a harbour complex area in Espirito Santo, southeastern Brazil. The objectives of this study were to evaluate the organotin pollution in the area and to assess the sensitivity of both species to BT pollution through imposex development. Specimens were collected between 2007 and 2010 and the sediments were sampled in 2007. Imposex intensity was evaluated based on the percentage of imposexed females, the relative penis length index (RPLI) and the average value of the vas deferens sequence index (VDSI). BTs were analysed using gas chromatography equipped with a pulsed flame photometric detector (PFPD). The results demonstrated the occurrence of elevated concentrations of BTs in the sediment ranging from 383.7 to 7172.9 ng Sn g(-1), indicating a severe contamination of the area, which was confirmed by the biological monitoring results. Our findings also indicated that L. ocellata is a less sensitive species than L. nassa and that this differential sensitivity plays a key role in the distributions of the populations of both species in the studied area.

Road-kills of the giant anteater in south-eastern Brazil: 10 years monitoring spatial and temporal determinants

Context. The giant anteater, Myrmecophaga tridactyla, is a large insectivorous mammal from Cerrado which is classified as vulnerable by the IUCN's red list. In spite of frequent giant anteater casualties, there continues to be a lack of published data on how road and landscape attributes affect road-kill rates - information that could prove useful in guiding mitigation measures.Aims. We seek to determine whether road and landscape attributes influence the incidence of road-kills of the giant anteater.Methods. From February 2002 to December 2012 (except for 2004), five roads in two regions in south-eastern Brazil were surveyed twice each month by car. We recorded temporal road-kill data for the giant anteater and related spatial road variables. These variables were also recorded at regular control sites every 2 km. We also took traffic volume data on stretches of the two roads to correlate with road-kills.Key results. Of the 45 anteater casualties recorded, there was a predominance of adult males. On roads MG-428 and SP-334, we found anteater road-kills were more common in the dry season, negatively correlated with traffic volume and related to the presence of native vegetation. Accordingly, road-kill sites tended to occur near the cerrado and grasslands and also appeared more frequently on some straight stretches of roadways. Although it was not shown to influence road-kill rates, topography data does point to regular overpass/underpass locations allowing population connectivity. Termitaria or ant nests were present at all road-kill sites, with 86% having signs of feeding.Conclusions. Native vegetation along roadways, together with straight road design, increases the probability of anteater road-kills by 40.1%.Implications. For mitigation, mowing and removing insect nests on roadsides, as well as roadside wildlife fencing in cerrado and grassland areas is suggested. Warning signs and radar to reduce vehicle speed are recommended for both human safety and anteater conservation. With regard to population connectivity, the absence of aggregated anteater road-kill data in this study meant that there were no particular crossing locations identified. However, the collected topography data do show places that could be used for roadway crossings. The measures indicated may apply to similar species and types of topography on other continents.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

Conformance monitoring, contract of objectives, tube in the sky

Ponencia invitada sobre asignacion y gestion de losts en el curso de verano de la UPM Research in Decisión Support Systems for future Air Traffic Management

Analysis of Contributions to NO2 Ambient Air Quality Levels in Madrid City (Spain) through Modeling. Implications for the Development of Policies and Air Quality Monitoring

As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large cities across Europe, particularly for NO2. Modeling air quality in urban areas is rather complex since observed concentration values are a consequence of the interaction of multiple sources and processes that involve a wide range of spatial and temporal scales. Besides a consistent and robust multi-scale modeling system, comprehensive and flexible emission inventories are needed. This paper discusses the application of the WRF-SMOKE-CMAQ system to the Madrid city (Spain) to assess the contribution of the main emitting sectors in the region. A detailed emission inventory was compiled for this purpose. This inventory relies on bottom-up methods for the most important sources. It is coupled with the regional traffic model and it makes use of an extensive database of industrial, commercial and residential combustion plants. Less relevant sources are downscaled from national or regional inventories. This paper reports the methodology and main results of the source apportionment study performed to understand the origin of pollution (main sectors and geographical areas) and define clear targets for the abatement strategy. Finally the structure of the air quality monitoring is analyzed and discussed to identify options to improve the monitoring strategy not only in the Madrid city but the whole metropolitan area.

Accident trend monitoring and exploratory analysis. Final report.

National Highway Traffic Safety Administration, National Center for Statistics and Analysis, Washington, D.C.

Procedures for monitoring the importation of motor vehicles and motor vehicle equipment : as provided in Title 19-Code of Federal Regulations 12.80.

DOT HS 803 546

Development of an eddy current noncontact displacement gage for monitoring anthropomorphic dummy chest deflections. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness.

National Highway Traffic Safety Administration, Washington, D.C.

RSV test monitoring and data publication - Renault R20TS-to-Calspan RSV 75 degree right side impact at Renault. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

RSV test monitoring and data publication - Renault R20TS-to-Calspan RSV 75 degree left side impact at Peugeot. Final report.

National Highway Traffic Safety Administration, Washington, D.C.