• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.262-264
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• 1999

이동 통신 텔레트래픽 모델은 Traffic Source 모델과 Network Traffic 모델이라는 2개의 하부 모델로 구성된다. 본 논문에서는 기지국이 설치되어 있는 지역특성을 고려한 Network Traffic 모델을 제시한다. 기존의 Network Traffic 모델에서는 이동 통신 환경을 시뮬레이션 하기 위해 동일한 환경에 설치되어 있는 몇 개의 기지국을 가정하여 제시하고 있기 때문에, 기지국이 설치되어 있는 지역적 특성에 따라 다른 사용자 호 특성 및 설치 지역 특성이 전혀 반영되지 않고 있다. 도시를 상업, 주거, 준공업, 그리고 녹지지역으로 되어 있는 도시 계획 용도지역과 이외에 특이한 호 발생 패턴이 예측되는 역과 터널 주변이라는 6개의 지역으로 구분하고, 여기에 설치되어 있는 기지국으로부터 실제 데이터를 수집하였다. 이 자료를 이용하여 기지국이 설치되어 있는 지역에 따라 이동 통신 기지국의 요일별 통화량 분포를 분석하였으며, 이를 시뮬레이터에 적용하기 위한 평균값 및 분포값을 제시하였다. 이 파라메터들은 이동통신 시스템의 성능 및 신뢰성을 측정하기 위한 매우 중요한 값들이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3098-3103
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• 2009

In this paper, self-similar characteristics over statistical approaches and real-time Ethernet network traffic measurements are estimated. It is also shown that the self-similar traffic reflects real Ethernet traffic chareacteristics by comparing TCP-MT source model which is exactly self-similar model to the traditional Poisson model.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.67-72
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• 2021

MANET(Mobile Ad Hoc Network) is the structure in which a source node communicates with a destination node by establishing a route with neighbor nodes without using the existing wired or wireless network. Therefore, the routing protocol for MANET must correspond well to changes in the channel state of moving nodes, and should have simple operation, high reliability, and no routing loop. In this paper, the simulation was perform by using a traffic model with on/off two states provided by the NS-3 network simulator. Also, the duration of the ON state and the duration of the OFF state used the traffic where inter arrival time of data is irregular by generating random values with constant, exponential distribution, and Pareto distribution. The performance of the DSDV, OLSR, and AODV protocols was compare and analyzed using the generated traffic model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.335-341
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• 2018

The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.9-16
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• 2021

Recently, the traffic volume of mobile communications increases rapidly and the small-cell is one of the solutions using two offload schemes, i.e., local IP access (LIPA) and selected IP traffic offload (SIPTO), to reduce the end-to-end delay and amount of mobile data traffic in the core network (CN). However, 3GPP describes the concept of LIPA and SIPTO and there is no decision algorithm to decide the path from source nodes (SNs) to destination nodes (DNs). Therefore, this paper proposes a dynamic mobile data traffic offload scheme using small-cells to decide the path based on the SN and DN, i.e., macro user equipment, small-cell user equipment (SUE), and multimedia server, and type of the mobile data traffic for the real-time and non-real-time. Through analytical models, it is shown that the proposed offload scheme outperforms the conventional small-cell network in terms of the delay of end-to-end mobile data communications and probability of the mobile data traffic in the CN for the heterogeneous networks.

• Journal of Korean Society of Transportation
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• v.12 no.3
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• pp.49-64
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• 1994

The development of a mathematical modular framework based on Petri Network theory to model a traffic network is the subject of this paper. Traffic intersections are the primitive elements of a transportation network and are characterized as event driven and asynchronous systems. Petri network have been utilized to model these discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issuse. The Petri-net based model of a generic traffic junction is presented. These modular networks are effective in synchronizing their components and can be used for modeling purposes of an asynchronous large scale transportation system. The derived model is suitable for simulations on a multiprocessor computer since its program execution safety is secured. The software pseudocode for simulating a transportation network model on a multiprocessor system is presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.575-585
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• 2015

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 significant drawbacks in high-speed network environment that the processing speed is much slower than other methods such as header-based and statistical methods. In addition, as signature numbers are increasing, traffic analysis speed also declines because of signature matching method that does not consider analytic efficiency of each signature and traffic occurrence feature. In this paper, we propose a signature list reordering method in order by analytic value of each signature. When we reordered the signature list by the proposed method, we achieved about 30% improvement in speed of the traffic analysis compared with random signature list.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.350-359
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• 2014

With the rapid growth of Internet, the importance of application traffic analysis is increasing for efficient network management. The statistical information in traffic flows can be efficiently utilized for application traffic identification. However, the packet out-of-order and retransmission occurred at the traffic collection point reduces the performance of the statistics-based traffic analysis. In this paper, we propose a novel method to detect and resolve the packet out-of-order and retransmission problem in order to improve completeness and accuracy of the traffic identification. To prove the feasibility of the proposed method, we applied our method to a real traffic analysis system using statistical flow information, and compared the performance of the system with the selected 9 popular applications. The experiment showed maximum 4% of completeness growth in traffic bytes, which shows that the proposed method contributes to the analysis of heavy flow.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.574-581
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting their performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN, and VBR traffic characteristic have indicated that the models used in the traditional Poisson assumption cannot properly predict the real traffic properties due to underestimation of the long-range dependence of network traffics and self-similar properties. In this paper, It is also shown that the self-similar traffic reflects real Ethernet traffic characteristics by comparing Pareto-like ON/OFF source model which is exactly self-similar model to the traditional Poisson model. It is also performed optimization design and performance analysis of ATM network using Internet traffic characteristics.