• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.162-167
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• 1999

ATM technology is well developed. Small-scale access node and edge switches are introduced in the network. Large scale ATM core switches are prepared for backbone application. Currently, Internet traffic is increasing so rapidly and we need to consider effective way of accommodating the volume of traffic. In the other hand, QoS and traffic engineering concept is required in the Internet services. Here, in this paper, two technologies are explained and suggested for integration of networks for future ATM based IP network.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.71-82
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• 2020

Traffic accidents due to excessive speed frequently occur in places where traffic signal controllers are installed, places where sharp curves exist, or places where the traffic signal cycle does not match the current time. These traffic accidents cause economic loss due to the destruction of road facilities and structures, and cause a big problem of increasing the number of traffic accident deaths. When a traffic accident occurs, leaving a tire mark before or after a car crash, pre-collision speed of the car is calculated using the law of conservation of momentum or the skid mark formula. In the skip skid mark generated in ABS brake vehicles and the combshaped yaw mark generated by tire trace caused by lateral sliding, there is a difference of 30-40% in the reliability of the vehicle speed calculated by the smite mark. In this paper, we propose an algorithm that can improve the calculation reliability in vehicle speed by using skid marks in order to compensate for this problem. In addition, we present an intelligent speed calculation algorithm for traffic safety and a computer simulation in order to prevent traffic accidents by estimating the speed of a vehicle, using Skid marks, Yaw marks, and ABS brake characteristics and fuzzy rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2261-2280
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• 2014

Recently, network traffic has become more complex and diverse due to the emergence of new applications and services. Therefore, the importance of application-level traffic classification is increasing rapidly, and it has become a very popular research area. Although a lot of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in real-time application-level traffic classification. In this paper, we propose a novel application-level traffic classification method using payload size sequence (PSS) signature. The proposed method generates unique PSS signatures for each application using packet order, direction and payload size of the first N packets in a flow, and uses them to classify application traffic. The evaluation shows that this method can classify application traffic easily and quickly with high accuracy rates, over 99.97%. Furthermore, the method can also classify application traffic that uses the same application protocol or is encrypted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• 한국콘텐츠학회논문지
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• 제5권6호
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• pp.65-73
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• 2005

인터넷의 급속한 보급과 대용량 멀티미디어 서비스에 대한 요구가 나날이 증가하고 있다. 기존의 무선랜은 도달거리가 짧아서 가입자가 기지국(Base Station)에서 멀어지면 서비스 품질이 저하되고, 무선인터넷은 사용비용이 높다. 최근에 세계 최초로 국내에서 개발한 휴대 인터넷 시스템인 와이브로(WiBro: Wireless Broadband Internet) 시스템은 휴대폰과 무선 랜의 중간 영역에 위치한 이동초고속인터넷서비스이다. 본 논문에서는 와이브로 시스템의 근간을 이루는 IEEE 802.16 BWA을 기반으로 멀티미디어 서비스를 제공하기 위해서 서비스별 트래픽 모델과 특성을 분석하고, 다양한 트래픽 혼합 비율에 따라 시뮬레이션을 수행하였으며, 이를 토대로 최종적으로는 WiBro 시스템에서 셀 당 수용 가능한 최대 가입자 수를 산출하였다.

• 디지털융복합연구
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• 제13권11호
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• pp.179-185
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• 2015

본 논문에서는 네트워크와 TV기술의 융합분야인 IPTV에 관한 연구로써 인터넷 환경에서 망의 트래픽증가에 따른 IPTV의 영상품질의 성능평가를 수행하였다. 이를 위해 실제 인터넷망과 유사한 모의 인터넷망을 구축하여 트래픽증가에 따른 IPTV의 수신 영상의 품질을 V-Factor 측정모델을 사용하여 실험하였으며, 그 결과를 분석하였다. 본 논문에서 도출한 결과는 IPTV 방송에서 시청 가능한 화질의 V-Factor, 네트워크 성능 측정요소, 콘텐츠 관련 측정요소 등에 대한 임계치의 기준으로 활용할 수 있을 것으로 판단된다.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.28-34
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• 2021

Internet Service Providers (ISPs) constantly endeavor to resolve network congestion, in order to provide fast and cheap services to the customers. This study suggests two models based on Markov chain, using three and four access attempts to complete the call. It involves a comparative study of four models to check the relationship between Internet Access sharing traffic, and the possibility of network jamming. The first model is a Markov chain, based on call-by-call attempt, whereas the second is based on two attempts. Models III&IV suggested by the authors are based on the assumption of three and four attempts. The assessment reveals that sometimes by increasing the number of attempts for the same operator, the chances for the customers to complete the call, is also increased due to blocking probabilities. Three and four attempts express the actual relationship between traffic sharing and blocking probability based on Markov using MATLAB tools with initial probability values. The study reflects shouting results compared to I&II models using one and two attempts. The success ratio of the first model is 84.5%, and that of the second is 90.6% to complete the call, whereas models using three and four attempts have 94.95% and 95.12% respectively to complete the call.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.