• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.33-43
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• 2004

In this paper, a traffic trend analysis based SNMP algorithm is proposed for improving the problem of existing traffic analysis using SNMP. The existing traffic analysis method has a vulnerability that is taken much time In analyzing by using a threshold and not detected a harmful traffic at the point of transition. The method that is proposed in this paper can solve the problems that the existing method had, simultaneously using traffic trend analysis of the day, traffic trend analysis happening in each protocol and MIB object analysis responding to attacks instead of using the threshold. The algorithm proposed in this paper will analyze harmful traffic more quickly and more precisely; hence it can reduce the damage made by traffic flooding attacks. When traffic happens, it can detect the abnormality through the three analysis methods previously mentioned. After that, if abnormal traffic overlaps in at least two of the three methods, we can consider it as harmful traffic. The proposed algorithm will analyze harmful traffic more quickly and more precisely; hence it can reduce the damage made by traffic flooding attacks.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.221-229
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• 2007

Security of the port TCP/80 has been demanded by reason that the others besides web services have been rapidly increasing use of the port. Existing traffic analysis approaches can't distinguish web services traffic from application services when traffic passes though the port. monitoring method based on protocol and port analysis were weak in analyzing harmful traffic using the web port on account of being unable to distinguish payload. In this paper, we propose a method of detecting harmful traffic by web traffic analysis. To begin, traffic Capture by real time and classify by web traffic. Classed web traffic sorts each application service details and apply weight and detect harmful traffic. Finally, method propose and implement through coding. Therefore have a purpose of these paper to classify existing traffic analysis approaches was difficult web traffic classified normal traffic and harmful traffic and improved performance.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.41-53
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• 2009

For 5 years and 6 months, ranging from January 2003 to June 2008, SPSS 12.0 Statistical Program was used to analyze the overall analysis, analysis of center line encroachment, analysis of signal violations, analysis of drinking while intoxicated, analysis of driving without license, analysis of two-wheel vehicle, analysis of pedestrian, analysis of safety equipment, analysis of traffic publicity or education and automobile registration accounts, and casualty of traffic accidents that are determined as having statistical implication based on the statistics available from the policy to take a look at traffic accident in the Daejeon area, and there were some meaningful results. With the proof that there is a certain level of ratio for the correlations between traffic control and traffic accident that the effect of traffic control has shown with certain time interval. The relationship of traffic control and the casualty of traffic accident has very low coefficient of correlations that it is not statistically noticeable that traffic control of the police has almost no effect in preventing traffic accident. This is a display of the fact that the conversion of direction for traffic safety measure undertaken to this point is rather urgent that there is a dire need of establishing the effective alternatives.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• Journal of Navigation and Port Research
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• v.31 no.1 s.117
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• pp.43-48
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• 2007

Maritime traffic engineering is a technical field that observes the flow of vessels' traffic in accurate and describes the feature of ship's movement statistically, then contributes for the improvement of traffic flow and the safety of traffic. The flow of marine traffic can be controlled by carrying out assessment and analysis of vessel's traffic. It can realize the safety of marine traffic by accurate research and analysis of vessel's traffic, understanding its flow and analysis data of vessel traffic. This study shows the analysis system of marine traffic connected with Radar, AIS based on ENC(Electronic Navigational Chart). The marine traffic analysis system contributes to the safety of marine traffic through the design of marine traffic route, harbour facilities and improvement of vessels' traffic flow.

• ETRI Journal
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• v.27 no.1
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• pp.22-42
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• 2005

Traditional traffic identification methods based on wellknown port numbers are not appropriate for the identification of new types of Internet applications. This paper proposes a new method to identify current Internet traffic, which is a preliminary but essential step toward traffic characterization. We categorized most current network-based applications into several classes according to their traffic patterns. Then, using this categorization, we developed a flow grouping method that determines the application name of traffic flows. We have incorporated our method into NG-MON, a traffic analysis system, to analyze Internet traffic between our enterprise network and the Internet, and characterized all the traffic according to their application types.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.127-132
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• 2006

Maritime transportation engineering is a technical field that observes the flow of vessel's traffic in accurate and describes the feature of ship's movement statistically, then contributes to the improvement of traffic flow and the safety of traffic. The flow of marine traffic can be controlled by carrying out assessment and analysis of vessel's traffic. It can realize the safety of marine traffic by accurate research and analysis of vessel's traffic, understanding its flow and analysis data of vessel traffic. This study the analysis system of marine traffic connected with Radar, AIS based on ENC(Electronic Navigational Chart). The marine traffic analysis system contributes to safety of marine traffic through the design of marine traffic route, harbour facilities and improvement of vessel's traffic flow.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.