• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.279-288
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• 2004

Rapidly emerging P2P(Peer to Peer) applications generate very bursty traffic, which gives a lot of burden to network, and the amount of such traffic is increasing rapidly. Thus it is becoming more important to understand the characteristics of such traffic and reflect it when we design and analyze the network. To do that we measured the traffic in a campus network and present flow statistics and traffic models of the measured traffic, and compare them with those of the web traffic. The results indicate that P2P traffic is much burstier than web traffic and as a result it negatively affects network performance. We modeled P2P traffic using self-similar traffic model to predict packet delay and loss occurred in network which are very important to evaluate network performance. We also predict queue length distribution and loss probability in SSQ(Single Sewer Queue). To assess accuracy of traffic model, we compare the SSQ statistics of traffic models with that of the traffic trace. The results show that self-similar traffic models we use can predict P2P traffic behavior in network precisely. It is expected that the traffic models we derived can be used when we design network capacity and predict network performance and QoS of the P2P applications.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.49-51
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• 2003

Developers of network games have used several prediction techniques for hiding transmission delay to support the real­time requirement of network games. Nowadays many researches that are related with network game are in progress to solve delay problems more radically, such as to propose new routers architecture and transport protocols suitable to characteristics of network game traffic. So for these advanced researches the tasks to grasp the traffic characteristics of a network game are needed. In this paper we aimed to capture the traffic of MMORPG and present the statistical analysis of measured data. The measurement and the analysis were accomplished with the server of 'Lineage' that regarded as the most successful MMORPG. Next, we have implemented a traffic generator that reflects the characteristics of MMORPG and shown that the trace generated by MMORPG traffic generator had identical characteristics with actual traffic using statistical testing method. We expect that this traffic generator can be used in many researches related with a network game.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.123-131
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• 2016

The high-availability seamless redundancy (HSR) protocol is one of the most important redundancy IEC standards that has garnered a great deal of attention because it offers a redundancy with zero recovery time, which is a feature that is required by most of the modern substation, smart grid, and industrial field applications. However, the HSR protocol consumes a lot of network bandwidth compared to the Ethernet standard. This is due to the duplication process for every sent frame in the HSR networks. In this paper, a novel algorithm known as the reducing multicast traffic (RMT) is presented to reduce the unnecessary redundant multicast traffic in HSR networks by limiting the spreading of the multicast traffic to only the rings that have members associated with that traffic instead of spreading the traffic into all the network parts, as occurs in the standard HSR protocol. The mathematical and the simulation analyses show that the RMT algorithm offers a traffic reduction percentage with a range of about 60-87% compared to the standard HSR protocol. Consequently, the RMT algorithm will increase the network performance by freeing more bandwidth so as to reduce HSR network congestion and also to minimize any intervention from the network administrator that would be required when using traditional traffic filtering techniques.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.71-80
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• 2023

With the rapid advancement of the Internet, the use of encrypted traffic has surged in order to protect data during transmission. Simultaneously, network attacks have also begun to leverage encrypted traffic, leading to active research in the field of encrypted traffic analysis to overcome the limitations of traditional detection methods. In this paper, we provide an overview of the encrypted traffic analysis field, covering the analysis process, domains, models, evaluation methods, and research trends. Specifically, it focuses on the research trends in the field of anomaly detection in encrypted network traffic analysis. Furthermore, considerations for model development in encrypted traffic analysis are discussed, including traffic dataset composition, selection of traffic representation methods, creation of analysis models, and mitigation of AI model attacks. In the future, the volume of encrypted network traffic will continue to increase, particularly with a higher proportion of attack traffic utilizing encryption. Research on attack detection in such an environment must be consistently conducted to address these challenges.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.23-33
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• 2004

In this paper, we propose a novel traffic measurement based detection of network attack symptoms on high speed Internet backbone links. In order to do so, we characterize the traffic patterns from the normal and the network attacks appeared on Internet backbone links, and we derive two efficient measures for representing the network attack symptoms at aggregate traffic level. The two measures are the power spectrum and the ratio of packet counts to traffic volume of the aggregate traffic. And, we propose a new methodology to detect networks attack symptoms by measuring those traffic measures. Experimental results show that the proposed scheme can detect the network attack symptoms very exactly and quickly. Unlike existing methods based on Individual packets or flows, since the proposed method is operated on the aggregate traffic level. the computational complexity can be significantly reduced and applicable to high speed Internet backbone links.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.474-481
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• 2008

Scanning worm increases network traffic load and result in severe network congestion because it is a self-replicating worm and send copies of itself to a number of hosts through the Internet. So an early detection system which can automatically detect scanning worms is needed to protect network from those attacks. Although many studies are conducted to detect scanning worms, most of them are focusing on the method using packet header information. The method using packet header information has long detection delay since it must examine the header information of all packets entering or leaving the network. Therefore we propose an algorithm to detect scanning worms using network traffic characteristics such as variance of traffic volume, differentiated traffic volume, mean of differentiated traffic volume, and product of mean traffic volume and mean of differentiated traffic volume. We verified the proposed algorithm by analyzing the normal traffic captured in the real network and the worm traffic generated by simulator. The proposed algorithm can detect CodeRed and Slammer which are not detected by existing algorithm. In addition, all worms were detected in early stage: Slammer was detected in 4 seconds and CodeRed and Witty were detected in 11 seconds.

• ETRI Journal
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• v.20 no.2
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• pp.171-191
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• 1998

In this paper, we propose a new heuristic design algorithm for the virtual path (VP)-based ATM network with multiple traffic classes, in which QoS constraints associated with traffic class are taken into account. The minimum bandwidth of VP required to carry given amount of traffic is obtained by utilizing an equivalent bandwidth concept, and the route of each VP is placed so that the network cost is minimized while the QoS requirement is fulfilled To evaluate our design algorithm, we consider two kinds of traffic: voice traffic as low speed service and still picture traffic as high speed service. Through numerical examples, we demonstrate that our design method can achieve an efficient use of network resources, which results in providing a cost-effective VP-based ATM network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.5
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• pp.378-383
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• 2005

Traffic engineering is a technology that guarantees quality of service that users want and maximize inflection degree of network resources at the same time as evenly distributing traffic to whole network. To improve performance of network at traffic and resources level, traffic engineering aims at utilizing network resource efficiently and effectively and must be satisfied performance requirement concerned with traffic. In this paper, two models to embody traffic engineering are analyzed and finally functional structure of IP/WDM traffic engineering is explained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.