• 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.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.1 no.1
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• pp.51-64
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• 2002

This paper analyzes performance measures of a Bluetooth_based mobile home network system. The home network system consists of terminals with Bluetooth interfaces, access points (AP), a home PC, and a gateway A mobile host in wireless terminals uses Mobile IP for supporting the mobility This paper considers four types of data traffic, which are new connection traffic, handoff traffic, Internet data traffic, and control data traffic and suggests a queueing system model of the home network system, where the AP and the home PC are modeled as M/G/1 with four priority queues and the gateway is modeled as M/G/1 with a single queue The generation rate and service time of individual traffic influence their performance measures. Based ell the suggested model, we propose the elapsed time of data traffic in terms of the number of cells, the number of Home PCs, arrival rates of four types of traffic and the service rates of AP/Home PCs/Gateway To analyze influences on the elapsed time with respect to arrival rate of four types of traffic, some examples are given.

• International Journal of Highway Engineering
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• v.15 no.3
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.121-127
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• 2002

In this paper, we propose traffic management support and evaluate the performance through simulation. We suggest traffic management routing protocol that can guarantee reliance according to not only reduction of the Network traffic congestion but also distribution of the network load that prevents data transmission. For performance evaluation, we analyzed the average data reception rate and network load, considering the node mobility. We found that in the mobile Ad Hoc networks, the traffic management service increased the average data reception rate and reduced the network traffic congestion and network load in Mobile Ad Hoc Networks.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.327-330
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• 2007

This article discusses a capacity planning method in QoS-guaranteed IP networks such as BcN (Broadband convergence Network). Since IP based networks have been developed to transport best-effort data traffic, the introduction of multi-service component in BcN requires fundamental modifications in capacity planning and network dimensioning. In this article, we present the key issues of the capacity planning in multi-service IP networks. To provide a foundation for network dimensioning procedure, we describe a systematic approach for classification and modeling of BcN traffic based on the QoS requirements of BcN services. We propose a capacity planning framework considering data traffic and real-time streaming traffic separately. The multi-service Erlang model, an extension of the conventional Erlang B loss model, is introduced to determine required link capacity for the call based real-time streaming traffic. The application of multi-service Erlang model can provide significant improvement in network planning due to sharing of network bandwidth among the different services.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.27-34
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• 2009

Currently network users, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is designed and implemented based on a model and simulation using data engineering methodology to be avaiable in evaluating large network traffic data. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• 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.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.4
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• pp.131-140
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• 2010

Recently, mobile wireless data traffic has been dramatically increased due to not only the popularization of digital convergence devices including smart phone, Net-book, and Tablet PC, but also the vitalization of wireless Internet related eco-systems such as AppStore. In addition, it is expected that a tremendous increase in mobile data is caused by the release of unlimited mobile data plans (flat-fee). In order to deal with such mobile data traffic explosion, it is necessary that network operators should make efforts to offload wireless data traffic. This paper reviews the condition of mobile wireless data traffic in domestic and international telecommunication industry and looks for various action plans to overcome the difficulty of network operators.

• 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.