• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.32-38
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• 2018

Studies on classification of national roads have been continued, but there is little research on the classification of urban arterial roads. Due to the increase of traffic volume, urban arterial roads do not perform well as main roads. In this paper, the function of urban arterial road was established by using cluster analysis using traffic characteristics. Traffic characteristics such as traffic volume, weekend coefficient and speed coefficient were used to establish the functions of 55 main arterial roads in Seoul. The results of this paper are compared with those of the method using AADT. The method using AADT classifies the characteristics according to the traffic volume of the whole lane. In this paper, however, the results are derived using the traffic volume per lane reflecting the actual traffic volume. In addition, the functional classification of the arterial roads in Seoul was compared with the results of this paper to verify that the traffic characteristics were reflected. As a result, the method presented in this paper is more effective in showing traffic characteristics than the current highway functional classification method, and the functional classification system will be helpful for road extension and planning design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• ETRI Journal
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• v.28 no.6
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• pp.777-786
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• 2006

Since the introduction of competition in the telecommunication market due to the growth of the interconnection between heterogeneous networks, particularly fixed and mobile networks, the interconnection charge based on traffic-sensitive (TS) and non-traffic-sensitive (NTS) costs has become more important. Although there have been many studies of the public switched telephone network (PSTN), previous studies of TS and NTS costs in mobile networks are very few. In this paper, as a pilot study, we propose three criteria and a procedure for the classification of TS and NTS costs based on mobile systems. The three criteria are the following: function type, investment requirement, and main exhaust driver. Moreover, for a CDMA mobile system, strongly TS, strongly NTS, and mixed components are classified by the proposed criteria and procedure. The proposed criteria, procedure, and classification can provide a systematic and useful guideline to decide the scope of mobile facilities and to determine the terminating cost on mobile networks from fixed networks.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.61-64
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• 2024

As research on autonomous driving technology becomes more active, various studies on signal recognition of traffic lights are also being conducted. When recognizing traffic lights with different purposes and shapes, such as pedestrian traffic lights, vehicle-only traffic lights, and right-turn traffic lights, existing classification methods may cause misrecognition problems. Therefore, in this study, we studied a model that allows accurate signal recognition by subdividing the classification of signals according to the purpose and type of traffic lights. A signal recognition model was created by classifying traffic lights according to their shape and purpose into horizontal, vertical, right turn, etc., and by comparing them with the existing signal recognition model based on YOLOv5, it was confirmed that more correct and accurate recognition was possible.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.89-99
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• 2011

The payload signature-based traffic classification system has to deal with large amount of traffic data, as the number of internet-based applications and network traffic continue to grow. While a number of pattern-matching algorithms have been proposed to improve processing speedin the literature, the performance of pattern matching algorithms is restrictive and depends on the features of its input data. In this paper, we studied how to optimize the search space in order to improve the processing speed of the payload signature-based traffic classification system. Also, the feasibility of our design choices was proved via experimental evaluation on our campus traffic trace.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.307-322
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• 2010

Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.519-525
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• 2013

The traffic classification is a preliminary and essential step for stable network service provision and efficient network resource management. However, the payload signature-based method has a significant drawback in high-speed network environment that the processing speed is much slower than other method such as header-based and statistical methods. In this paper, We propose the server IP, Port cache-based traffic classification method using application traffic locality to improve the processing speed of traffic classification. The suggested method achieved about 10 folds improvement in processing speed and 10% improvement in completeness over the payload-based classification system.

• Journal of KIISE
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• v.44 no.4
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• pp.433-438
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• 2017

Presently, the ubiquitous emergence of high-speed-network environments has led to a rapid increase of various applications, leading to constantly complicated network traffic. To manage networks efficiently, the traffic classification of specific units is essential. While various traffic-classification methods have been studied, a methods for the complete classification of network traffic has not yet been developed. In this paper, a correlation model of the network flow is defined, and a traffic-classification method for which this model is used is proposed. The proposed network-correlation model for traffic classification consists of a similarity model and a connectivity model. Suggestion for the effectiveness of the proposed method is demonstrated in terms of accuracy and completeness through experiments.

• KNOM Review
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• v.22 no.1
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• pp.20-29
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• 2019

Recently, various machine learning based traffic classification methods are focused on detecting malicious network traffic. In this paper, convolutional neural network based malicious network traffic classification method is introduced and its performance is evaluated. In order to utilize the convolutional neural network which is excellent in analyzing images, a image transform method from important information of network traffic to a standardized image is proposed, and the transformed images are used as learning input of a CNN network traffic classifier. By using the real network traffic dataset, the proposed image transform method and CNN based network traffic classification method are evaluated. Especially, under various configurations of CNN, the performance of the proposed method is evaluated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1170-1178
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• 2010

Recently as the number of users and application traffic is increasing on high speed network, the importance of application traffic classification is growing more and more for efficient network resource management. Although a number of methods and algorithms for traffic classification have been introduced, they have some limitations in terms of accuracy and completeness. In this paper we propose an application traffic classification based multi-level architecture which integrates several signature-based methods and behavior algorithm, and analyzes traffic using correlation among traffic flows. By strengthening the strength and making up for the weakness of individual methods we could construct a flexible and robust multi-level classification system. Also, by experiments with our campus network traffic we proved the performance and validity of the proposed mechanism.