• Journal of KIISE:Information Networking
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• v.34 no.2
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• pp.81-87
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• 2007

This paper proposes architecture of Linux-based Network Traffic Control Test-bed (NTCT) that easily implements reconfigurable network environment. The proposed NTCT consists of traffic generator that uses the simulation results of NS2 simulator, traffic controller using Linux kernel, and traffic monitor. This paper also includes the analysis example using the proposed NTCT.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.688-695
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• 2013

Networked Control Systems(NCS) contain the structure which controllers, actuators and sensors are connected to communication network. And they have been adopted in large and complicated plant area due to the advantages of mitigating computational bottleneck and maintenance. Although this structure provides many benefits, it brings in problems of unpredictable communication delay, data loss and corruption. This phenomena have to be considered in designing NCSs since it affects on overall control system stability. This paper introduces network traffic test method for ethernet based NCSs to find out maximum network usage which guarantee stable control operation. Test results shows this methods can be adopted in various types of NCSs and contributes economical system design and effective system operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4125-4132
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• 2011

The research of image quality estimation standard and the image quality change according to the network traffic load increase at IPTV multicasting service is necessary because the concern of IPTV(Internet Protocol TV) service become active recently. In the research, for finding out the threshold value of network performance elements giving the effect to the image quality according to the network traffic load, we developed S/W to operate the test bed network and make the test scenario through test bed network test and then we expand the test environment scope to the college network and try to measure the image quality change of IPTV multicasting service according to the network traffic load increase at the college network similar to the real IPTV service environment.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.63-67
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• 2009

In this paper we propose a QoS policy, which is based on both DSCP and SPQ, appropriate to TPS users on optical subscriber network. Then we experiment and evaluate QoS policy through the test bed which emulates real optical subscriber network. In order to perform effective and real experiment on test bed we make test traffic equivalent to 400 TPS users and give it to test bed. The experimental result shows that no packet loss in real time service traffic such as voice, IPTV occurs during more than 4 hours. We think that our proposed QoS policy is a proper method which guarantees the service quality of real time services on optical subscriber network.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.107-115
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• 2009

Traditionally, simulation method is used to test and evaluate the performance of communication protocol or functional elements for mobile communication service. In this paper, wireless network simulator is implemented using the C++ object-oriented programming language. This simulator can simulate wireless data services, like as ad-hoc networks, by considering the user's mobility. In this paper, the simulator includes network traffic model to reflect wireless data service and traffic source model to represent a user's mobility similar to real service environment and traffic characteristics can be reflected on the simulation, and also more accurate simulation results can be got through that. In addition, by using object-oriented techniques, new service feature or environment can be easily added or changed so that the developed mobile communication simulator can reflect the real service environment all the time. This simulator can be used in adjusting the characteristics of wireless data hosts following the mobility of the user, and also can be used in building new wireless ad-hoc network routing protocols.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.