• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5B
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• pp.348-356
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• 2012

The application traffic analysis is getting more and more challenging due to the huge amount of traffic from high-speed network link and variety of applications running on wired and wireless Internet devices. Multi-level combination of various analysis methods is desired to achieve high completeness and accuracy of analysis results for a real-time analysis system, while requires much of processing burden on the contrary. This paper proposes a novel architecture for a real-time traffic analysis system which improves the processing performance on multi-core CPU environment. The main contribution of the proposed architecture is an efficient parallel processing mechanism with multiple threads of various analysis methods. The feasibility of the proposed architecture was proved by implementing and deploying it on our campus network.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.9-16
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• 2021

Recently, the traffic volume of mobile communications increases rapidly and the small-cell is one of the solutions using two offload schemes, i.e., local IP access (LIPA) and selected IP traffic offload (SIPTO), to reduce the end-to-end delay and amount of mobile data traffic in the core network (CN). However, 3GPP describes the concept of LIPA and SIPTO and there is no decision algorithm to decide the path from source nodes (SNs) to destination nodes (DNs). Therefore, this paper proposes a dynamic mobile data traffic offload scheme using small-cells to decide the path based on the SN and DN, i.e., macro user equipment, small-cell user equipment (SUE), and multimedia server, and type of the mobile data traffic for the real-time and non-real-time. Through analytical models, it is shown that the proposed offload scheme outperforms the conventional small-cell network in terms of the delay of end-to-end mobile data communications and probability of the mobile data traffic in the CN for the heterogeneous networks.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.543-552
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• 2008

ATIS(Advanced Traveler Information System) is the system to offer a real-time traffic information or traffic situation for the benefit of the client. One of traffic information collection methods for ATIS research is the method of image analysis. The method is divided into two : one is the method to set two loop detectors at the area and the other is the method detecting the vehicle through an image analysis. In this paper, we propose a real-time traffic information collection system to mix two methods. The system installs multiple virtual detection lines and traces the location of the vehicle. Use of multiple virtual detection lines supplements the defect of the method of loop detectors. And we drew a representative pixels in the detecting area and used it for image analysis. This is to solve the problem of time delay which increases as the image size increases. We gathered traffic images and experimented using the system and got 92.32% of detection accuracy.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.61-70
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• 2010

The purpose of this study is to analyze the accuracy of traffic volume forecast by comparing an estimated to real traffic volume. For this study, total 10 sections of national highways, which are planned in 1980s and 1990s, were selected and traffic analysis data for highway construction were collected. In addition, targeted 10 sections were categorized into network-related and -unrelated sections. In the analysis of inaccuracy between the estimated and real traffic, for network-related sections, appeared to have lower inaccuracy. As time goes on after traffic open, inaccuracy between the estimated and real traffic appeared to be lower. In various section lengths, the longer the section length, the higher the inaccuracy is. Using 3 years passed data after traffic open, national highway have lower inaccuracy than expressway. However, the traffic analysis according to traffic open time resulted in little change of the inaccuracy.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.421-431
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• 2022

The purpose of this research is to determine the point of fixation of the driver during the process of driving. Based on the results of this research, the driving instructor can make a judgement on what the trainee stare on the most. Traffic accidents have become a serious concern in modern society. Especially, the traffic accidents among unskilled and elderly drivers are at issue. A driver should put attention on the vehicles around, traffic signs, passersby, passengers, road situation and its dashboard. An eye-tracking-based application was developed to analyze the driver's gaze behavior. It is a prototype for real-time eye tracking for monitoring the point of interest of drivers in driving practice. In this study, the driver's attention was measured by capturing the movement of the eyes in real road driving conditions using these tools. As a result, dwelling duration time, entry time and the average of fixation of the eye gaze are leading parameters that could help us prove the idea of this study.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.995-999
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• 2010

Every year, traffic accidents and traffic congestion have been rapidly increasing, Although the roadway design and signal system have been improved to relieve traffic accidents, traffic casualties and property damage do not decrease. This paper develops a real-time traffic accident detection and analysis system (RTADAS): In the proposed system, we aim to precisely detect traffic accidents at different design and flow of intersections, However, because the data collected from intersections have uncertainty and complicated causal dependency between them, we construct probability-based networks for correct accident detection.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.859-876
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• 2010

In this paper, we propose a hierarchical application traffic classification system as an alternative means to overcome the limitations of the port number and payload based methodologies, which are traditionally considered traffic classification methods. The proposed system is a new classification model that hierarchically combines a binary classifier SVM and Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset from the bi-directional traffic flows generated by our traffic analysis system (KU-MON) that enables real-time collection and analysis of campus traffic. The system is composed of three layers: The first layer is a binary classifier SVM that performs rapid classification between P2P and non-P2P traffic. The second layer classifies P2P traffic into file-sharing, messenger and TV, based on three SVDDs. The third layer performs specialized classification of all individual application traffic types. Since the proposed system enables both coarse- and fine-grained classification, it can guarantee efficient resource management, such as a stable network environment, seamless bandwidth guarantee and appropriate QoS. Moreover, even when a new application emerges, it can be easily adapted for incremental updating and scaling. Only additional training for the new part of the application traffic is needed instead of retraining the entire system. The performance of the proposed system is validated via experiments which confirm that its recall and precision measures are satisfactory.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.69-75
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• 2002

In this paper, we propose the realtime network traffic monitoring system usin SNMP that can supported network and system operation, management, expansion, and design using network analysis and diagnosis to a network administrator. The proposed system consists of two parts: analysis server for collection and analysis of the network information, and supports real-time monitoring of network traffic, and client system shows user a graphical data that analyzed a returned result from the server. This system implements web-based technology using Java and contributes to enhance the effectiveness of network administrator's management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3098-3103
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• 2009

In this paper, self-similar characteristics over statistical approaches and real-time Ethernet network traffic measurements are estimated. It is also shown that the self-similar traffic reflects real Ethernet traffic chareacteristics by comparing TCP-MT source model which is exactly self-similar model to the traditional Poisson model.