• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.157-163
• /
• 2009

With the wide usage of internet in many fields, networks are being exposed to many security threats, such as DDoS attack and worm/virus. For enterprise network, prevention failure of network security causes the revealing of commercial information or interruption of network services. In this paper, we propose a method of prevention of DDoS attacks for enterprise network based on traceback and network traffic analysis. The model of traceback implements the detection of IP spoofing attacks by the cooperation of trusted adjacent host, and the method of network traffic analysis implements the detection of DDoS attacks by analyzing the traffic characteristic. Moreover, we present the result of the experiments, and compare the method with other methods. The result demonstrates that the method can effectively detect and block DDoS attacks and IP spoofing attacks.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2000.05a
• /
• pp.263-267
• /
• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real WAN delay time, TCP packet size, and the total network usage.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.2
• /
• pp.289-295
• /
• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics. Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real WAN delay time, TCP packet size, and the total network usage.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.1931-1936
• /
• 2007

Demand forecasting for railroad traffic is fairly important to establish future policy and plan. The future demand of railroad traffic can be predicted by analyzing the demand of air, marine and bus traffic which influence the demand of railroad traffic. In this study, forecasting the demand of railroad traffic is implemented through neural network using the demand of air, marine and bus traffic. Estimate accuracy of the demand of railroad traffic was shown about 84% through neural net model proposed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.6 no.5
• /
• pp.378-383
• /
• 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 Korean Operations Research and Management Science Society
• /
• v.34 no.1
• /
• pp.61-83
• /
• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.420-437
• /
• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.6B
• /
• pp.1165-1173
• /
• 1999

In order to support various multimedia communication services, a well balanced backbone network should be designed using recently advanced optical communication technologies. In this paper an optimal backbone network configuration design is presented fur Korean traffic environment. A new traffic model, Population-Distance-Gross Group Products(PDG) traffic model, is devised. In Korean network traffic environment, six regional centers are selected, link capacities between the regional centers are estimated from the PDG traffic model, and the overall network configuration is designed for the all-optical backbone network in Korea. A simulation study is carried out to verify the desired performance of the designed backbone network. Simulation results show that performance of the backbone network is well balanced to support various communication services in Korea in the 2000s.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.