• 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 Information Processing Systems
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• v.20 no.1
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• pp.131-147
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• 2024

A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.17-22
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• 2019

Network attack analysis and visualization methods using network traffic session data detect network anomalies by visualizing the sender's and receiver's IP addresses and the relationship between them. The traffic flow is a critical feature in detecting anomalies, but simply visualizing the source and destination IP addresses symmetrically from up-down or left-right would become a problematic factor for the analysis. Also, there is a risk of losing timely security situation when designing a visualization interface without considering the temporal characteristics of time-series traffic sessions. In this paper, we propose a visualization interface and analysis method that visualizes time-series traffic data by using the radial axis, divide IP addresses into network and host portions which then projects on the cylindrical coordinate system that could effectively monitor network attacks. The proposed method has the advantage of intuitively recognizing network attacks and identifying attack activity over time.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.917-921
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• 2004

Principle of mailing list was distributed messages to all subscribers in one time. But mailing list operation has constructed a network traffic problem. Because mailing list manager distributed mails without concentrate on subscriber network. If our network has many of subscribers, there will be redundant data in traffic channel. Submailing list has purpose to reduce problems. Analyses of mailing list characteristic in electronic mail were a feature of submailing list system, which manage by human hand (Network Administrator). That will cause trouble for network traffic if Network Administrator could not seek for mailing list characteristic from e-mails in due time. This article will present ideas and recognize methodology for automatic working in submailing list system. Recognize step begin with capture process, which use to trap e-mail information from transfer channel. Next process is preparing raw data into recognition format. Then the third one is recognize part and find out confidential factor. The last process is make decision and determine which electronic mail has properties of mailing list characteristic. Afterward deliver result to submailing list for carry on.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.349-352
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• 2001

In this paper, the design of WDM network using the traffic estimation modeling is implemented. Because of the lack of data of real traffic volumes, the information of statistic data is used. Using the modeling results, the WDM channels are assinged for each node, and the network is simulated using OPNET simulation tools. Here, we investigate the shortest routing scheme using OPNET simulation tools. As a result the realistic WDM network design for Korea topology is proposed.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.267-271
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• 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 network Traffic.

• Journal of Digital Contents Society
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• v.5 no.1
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• pp.7-11
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• 2004

There are many problems that must solve to construct next generation high-speed communication network. Among these, item that must consider basically is characteristics analysis of traffic that nows to network Traffic characteristics of many Internet services that is offered present have shown that network traffic exhibits at a wide range of scals-self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all, for design and dimensioning of next generation communication network, traffic model that are reflected burstiness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, the calculation equation is derived considering queueing delay and self-similarity of data traffic art compared with simulation results.

• Convergence Security Journal
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• v.16 no.5
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• pp.41-48
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• 2016

In this paper we propose a new method of security visualization, VisFlow, using traffic flows to solve the problems of existing traffic flows based visualization techniques that were a loss of end-to-end semantics of communication, reflection problem by symmetrical address coordinates space, and intuitive loss problem in mass of traffic. VisFlow, a simple and effective security visualization interface, can do a real-time analysis and monitoring the situation in the managed network with visualizing a variety of network behavior not seen in the individual traffic data that can be shaped into patterns. This is a way to increase the intuitiveness and usability by identifying the role of nodes and by visualizing the highlighted or simplified information based on their importance in 2D/3D space. In addition, it monitor the network security situation as a way to increase the informational effectively using the asymmetrical connecting line based on IP addresses between pairs of nodes. Administrator can do a real-time analysis and monitoring the situation in the managed network using VisFlow, it makes to effectively investigate the massive traffic data and is easy to intuitively understand the entire network situation.