• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3771-3792
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• 2021

With the rapid development of science and technology, several high-performance networks have emerged with various new applications. Consequently, financially or socially motivated attacks on specific networks have also steadily become more complicated and sophisticated. To reduce the damage caused by such attacks, administration of network traffic flow in real-time and precise analysis of past attack traffic have become imperative. Although various traffic analysis methods have been studied recently, they continue to suffer from performance limitations and are generally too complicated to apply in existing systems. To address this problem, we propose a method to calculate the correlation between the malicious and normal flows and classify attack traffics based on the corresponding correlation values. In order to evaluate the performance of the proposed method, we conducted several experiments using examples of real malicious traffic and normal traffic. The evaluation was performed with respect to three metrics: recall, precision, and f-measure. The experimental results verified high performance of the proposed method with respect to first two metrics.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.97-116
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• 1999

The growing demand for the real time traffic information is bringing about the category and number of traffic collection mechanism in the era of ITS. There are, however, two problems in making data into information using various traffic data. First, the information making process of making data into the representative information, for each traffic collection mechanism, for the specified analysis periods is required. Second, the integration process of fusing each representative information into "the information" for each link out of each source is also required. That is, both data reduction and/or data to information process and information fusion are required. This article is focusing on the development of information fusing algorithm based on voting technique, fuzzy regression, and, Bayesian pooling technique for estimating the dynamic link travel time of networks. The proposed algorithm has been validated using the field experiment data out of GPS probes and detectors over the roadways and the estimated link travel time from the algorithm is proved to be more useful than the mere arithmetic mean from each traffic source.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11B
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• pp.960-968
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• 2006

HMIPv6 improved the handover management of basic MIPv6 by introducing the new protocol agent MAP. In this new protocol, MAP instead of the Mobile Node intercepts all packets and redirects the packets to CoA of the Mobile Node. However, this process may degrade the network performance due to the centralization phenomenon of registration occurring in the hierarchical MAP structure. ffe propose two schemes to improve real time traffic performance. First proposal is a MAP selection mettled in which MAP is selected based on traffic characteristics. And we also propose differentiated traffic processing scheme with multi-level queues when Home Agent or Correspondent Nodes process Binding Update messages. Performances of the proposed scheme are analyzed. Analysis result shows that our model has good performance in the respect of location update cost and total cost of Mobile Nodes.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.437-447
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• 2020

Following the extension of human life expectancy, the number of elderly traffic accidents that have been increasing at a rapid pace since 2018 has also emerged as a social problem. The traffic accident rate among those aged 65 and older is increasing, but traffic safety policies are insufficient. Based on the analysis of traffic accident status for senior citizens and traffic accident for the past five years from 2014, the reduction plan is to be presented in three main aspects. First, the system needs systematic management by strengthening the system of senior citizens' transport policy departments and driver's license for senior citizens in government agencies, such as the United States, Britain and Japan, from an institutional perspective, so that the walking time and crosswalk traffic environment for the vulnerable should be improved from an environmental perspective. In addition, in human terms, the ability to cope with real-time changes in traffic conditions should be enhanced by training transportation safety experts to secure the effectiveness of education for elderly drivers and by strengthening safety education for those with driver's license and expanding experienced traffic safety facilities to enhance the ability of senior citizens to cope with the changing traffic conditions in real time.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.3
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• pp.171-190
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• 2008

With growing applications of wireless video streaming, an efficient video traffic model featuring modern high-compression techniques is more desirable than ever, because the wireless channel bandwidths are ever limited and time-varying. We propose a modeling and analysis method for video traffic by a class of stochastic processes, which we call '$GEO^Y/G/{\infty}$ input processes'. We model video traffic by $GEO^Y/G/{\infty}$ input process with gamma-distributed batch sizes Y and Weibull-like autocorrelation function. Using four real-encoded, full-length video traces including action movies, a drama, and an animation, we evaluate our modeling performance against existing model, transformed-M/G/${\infty}$ input process, which is one of most recently proposed video modeling methods in the literature. Our proposed $GEO^Y/G/{\infty}$ model is observed to consistently provide conservative performance predictions, in terms of packet loss ratio, within acceptable error at various traffic loads of interest in practical multimedia streaming systems, while the existing transformed-M/G/${\infty}$ fails. For real-time implementation of our model, we analyze G/D/1/K queueing systems with $GEO^Y/G/{\infty}$ input process to upper estimate the packet loss probabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.136-151
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• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.14-24
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• 2006

There are many problems and limits in equipments being used for traffic-volume analysis in the country. And traffic-volume information acquired through existing equipments is not provided in real-time. In the case of urban, there are limits on guarantee of trust on comprehending a appropriate road-volume because of difficulty on analyzing traffic-volume density and time series. And it is difficult to applicate in deciding a road policy as existing equipments don't provide the control information of traffic-flow. Therefore, it is necessary to build a road-flow policy rapidly and accurately through the road-flow information that analyze post-processed statistics data using traffic-flow investigation based on real time. In this study, we developed TICS(Traffic Information Collection System) based on GPS which could transmit traffic information transformed from car location information to traffic control center. And we developed TCS(Traffic Control System) based on Web GIS, which could manage and analyze transmitted traffic information, and it could offer handled road-flow information to Web-site in realtime.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.835-845
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• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.141-144
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• 2004

Aids to Navigation have been operated and placed along coasts and navigable waters as guides to mark safe water and to assist mariners in determining their position in relation to land and hidden dangers, controled on the basis of the maine traffic survey. The traditional survey have been conducted by some methods like an ocular observation using portable radar, a on-the-spot survey, a questionnaire. But these methods must have a lot of manpower and expenses. In this paper, we have developed the module which have some real time processing functions like making a database of radar image using PC camera, saving of the vessel's track, analysis if the maine traffic tendency and the distribution of density.