• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.79-81
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• 1987

The traffic characteristicsof digitalswich network depend on the structure blocking probability, path searching method and etc. This paper presents the study of TDX-1 swicth network traffic capacity by considering some decisive factors such as call processing software, switch network structrure and control schme Conclusively the study shows that the switch network of TDX-1 can handle approximately up to 1650 Erlang.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.283-294
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• 2006

Hostile network traffic is often different from normal traffic in ways that can be distinguished without knowing the exact nature of the attack. In this paper, we propose a new anomaly detection method using inbound network traffic distributions. For this purpose, we first characterize the traffic of a real campus network by the distributions of IP protocols, packet length, destination IP/port addresses, TTL value, TCP SYN packet, and fragment packet. And then we introduce the concept of entropy to transform the obtained baseline traffic distributions into manageable values. Finally, we can detect the anomalies by the difference of entropies between the current and baseline distributions. In particular, we apply the well-known denial-of-service attacks to a real campus network and show the experimental results.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.1-8
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• 2015

Security has become an issue due to the rapid increases in internet traffic data network. Especially P2P traffic data poses a great challenge to network systems administrators. Preemptive measures are necessary for network quality of service(QoS) and efficient resource management like blocking suspicious traffic data. Deep packet inspection(DPI) is the most exact way to detect an intrusion but it may pose a private security problem that requires time. We used several machine learning methods to compare the performance in classifying network traffic data accurately over time. The Random Forest method shows an excellent performance in both accuracy and time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3789-3809
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• 2015

In this paper, we deal with the problem of M2M gateways' network selection for different types of M2M traffic in heterogeneous wireless networks. Based on the difference in traffic's quality of service (QoS) requirements, the M2M traffic produced by various applications is mainly classified as two categories: flexible traffic and rigid traffic. Then, game theory is adopted to solve the problem of network-channel selection with the coexistence of flexible and rigid traffic, named as flexible network access (FNA). We prove the formulated discrete game is a potential game. The existence and feasibility of the Nash equilibrium (NE) of the proposed game are also analyzed. Then, an iterative algorithm based on optimal reaction criterion and a distributed algorithm with limited feedback based on learning automata are presented to obtain the NE of the proposed game. In simulations, the proposed iterative algorithm can achieve a near optimal sum utility of whole network with low complexity compared to the exhaustive search. In addition, the simulation results show that our proposed algorithms outperform existing methods in terms of sum utility and load balance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.296-304
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• 2005

ATCoP(Abnormal traffic controller based on prediction) is presented to securely support reliable Internet service and to guarantee network survivability, which is deployed in Internet access point. ATCoP is a method to control abnormal traffic that is entering into the network When unknown attack generates excessive traffic, service survivability is guaranteed by giving the priority to normal traffic than abnormal traffic, that is reserving some channels for normal traffic. If the reserved channel number increases, abnormal traffic has lower quality service by ATCoP system and then its service survivability becomes worse. As an analytic result, the proposed scheme maintains the blocking probability of normal traffic on the predefined level in the specific interval of input traffic.

• 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.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.613-624
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• 2002

Within the past decade, TCP/IP network environment has been explosively widespread all over the world. As the internet and the WWW expand their boundaries, the network traffic caused by data transfers over the internet has also increased. In this paper, we present the design and implementation of a WebTraMAS (Web-based Traffic Monitoring and Analysis System) which can resolve the shortcomings of current management approaches, particularly on the network traffic monitoring and analysis. The WebTraMAS presented in this paper performs the network management activities based on the parameters related to the MIB-II of SNMP and the parameters related to the QoS such as network performance and fault. The proposed WebTraMAS, implemented using the WWW technology, is able for the network manager to manage the network easily and platform independently with the performance analysis of internet traffic.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.759-766
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• 2020

In recent years, the maritime traffic environment has been changing in various ways, and the traffic volume has been increasing constantly. Accordingly, the requirements for maritime traffic analysis have become diversified. To this end, traffic characteristics must first be analyzed using vessel trajectory data. However, as the conventional method is mostly manual, it requires a considerable amount of time and effort, and errors may occur during data processing. In addition, ensuring the reliability of the analysis results is difficult, because this method considers the subjective opinion of analysts. Therefore, in this paper, we propose an automated method of traffic network generation for maritime traffic analysis. In the experiment, spatiotemporal features are analyzed using data collected at Mokpo Harbor over six months. The proposed method can automatically generate a traffic network reflecting the traffic characteristics of the experimental area. In addition, it can be applied to a large amount of trajectory data. Finally, as the spatiotemporal characteristics can be analyzed using the traffic network, the proposed method is expected to be used in various maritime traffic analyses.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.909-912
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• 1998

In this paper, a new connection admission controller using neural network is presented. The controller measures traffic flow, cell loss rate, and cell delay periodically. Using those measured information, it learns the distributions of traffics of each traffic. Also the proposed controller is able to measure and manage the delays that source traffics experience through the network by using DWRR multiplexer with buffers dedicated to each traffic source. Experimental result show that the heterogeneous traffic sources with various QoS requirement.