• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.26-38
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• 2016

Recently, mobile traffic has increased tremendously due to the deployment of smart devices such as smartphones and smart tablets. Android is the world's most powerful mobile platform in smartphone. The Android operating system provide seamless access to many applications and access to the Internet. It would involve network packet sharing communicated over the network. Network packet contains a lot of useful information about network activity that can be used as a description of the general network behaviours. To study what is the behaviours of the network packet, an effective tools such as network packet analyzers software used by network administrators to capture and analyze the network information. In this research, more understanding about network information in live network packet captured from Android smartphone is the target and identify the best network analyzer software.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.85-95
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• 2009

Congestion on expressways is increasing in spite of continuous road construction. In enlargement of expressway capacity to lessen congestion, a long period is needed and in the case of traffic congestion, it would be impossible to avoid long periods of traffic congestion. So, it is necessary to cope with traffic congestion through continuous traffic condition monitoring, analysis of the causes of congestion and the development of alternatives before traffic conditions worsen. A congestion index that can express traffic operating conditions measurably is needed to monitor those conditions. Thus, in this research, a new congestion index, the Traffic Condition Index (TCI), is developed. TCI is able to evaluate roads that have different grades (or design speeds) and to judge traffic condition as good, fair and poor (congested). In addition, TCI has merits in that it can strengthen the function of existing Freeway Traffic Management Systems (FTMS) and can be applied to congestion management easily: TCI calculates congestion intensity and severity using data obtained from existing FTMS. In order to validate TCI, it was applied to the Kyungbu Expressway and the Seohaean Expressway. As a result, TCI shows a good performance in the aspect of applicability and ability of presentation of traffic conditions compared with travel speed and Travel Time Index (TTI).

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.427-427
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• 2021

During the rainy season, heavy downpours are always a source of concern for the world. Flooding and heavy rains can devastate communities, disrupt agriculture, and contribute to traffic accidents.. Weir and flow hall effect sensors are the conventional analytical methods for measuring flow rate; in this paper, we analyzed manhole flowrate statistics. The measurement of the flow rate of a notch/weir is a time-consuming task that necessitates continuous mathematical analysis. . We created three types of IoT sensors in this study: (HC-SR04 ultrasonic, YF-S201 magnetic, and HB100 radar), which take the sensor's real-time input signal and estimate the flow using a notch equation and a previously calibrated optimized coefficient of discharge. The proposed systems are cost-effective, but in terms of accuracy, we found that the HC-SR04 ultrasonic sensor is the best of the three systems

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1771-1779
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• 2016

In these days, the world is getting connected to the internet like a sophisticated net, such an environment gives a suitable environment for cyber attackers, so-called cyber-terrorists. As a result, a number of cyber attacks has significantly increased and researches to find cyber attack traffics in the field of network monitoring has also been proceeding. But cyber attack traffics have been appearing in new forms in every attack making it harder to monitor. This paper suggests a method of tracking down cyber attack traffic sources by defining relational information flow of traffic data from highest cascaded and grouped relational flow. The result of applying this cyber attack source tracking method to real cyber attack traffic, was found to be reliable with quality results.

• Journal of Digital Contents Society
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• v.5 no.2
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• pp.151-156
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• 2004

This paper presents process model to accomplish traffic engineering in Internet. The process model consists of 4 stages. The first stage is the formulation of a control policy dominated network operation. The second stage is the observation of the network state through a set or monitoring functions. The third stage is the characterization or traffic and analysis or the network state. The final stage is the optimization of network performance. the four stages of the process model defined above are iterated.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.33-40
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• 2012

In this paper we propose an anomaly detection scheme to detect new attack paths or new attack methods without false positives by monitoring HTTP Outbound Traffic after efficient training. Our proposed scheme detects web-based attacks by comparing tags or javascripts of HTTP Outbound Traffic with normal behavioral models which apply HMM(Hidden Markov Model). Through the verification analysis under the real-attacked environment, we show that our scheme has superior detection capability of 0.0001% false positive and 96% detection rate.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1841-1848
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• 2013

In this paper, the plans for improvement of real-time security monitoring accuracy and expansion of control region were investigated through comprehensive and systematic collection and analysis of the anomalous activities that inflow and outflow in the network on a large scale in order to overcome the existing security monitoring system based on stylized detection patterns which could correspond to only very limited cyber attacks. This study established an anomaly observation system to collect, store and analyze a diverse infringement threat information flowing into the darknet network, and presented the information classification system of cyber threats, unknown anomalies and high-risk anomalous activities through the statistics based trend analysis of hacking. If this security monitoring system utilizing darknet traffic as presented in the study is applied, it was indicated that detection of all infringement threats was increased by 12.6 percent compared with conventional case and 120 kinds of new type and varietal attacks that could not be detected in the past were detected.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.600-607
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• 2011

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic identification becomes important for the effective use of network resources. In this paper, we present an Internet application traffic identification method based on flow correlation to overcome limitation of signature-based identification methods and to improve performance (completeness) of it. The proposed method can identify unidentified flows from signature-based method using flow correlation between identified and unidentified flows. We propose four separate correlation methods such as Server-Client, Time, Host-Host, and Statistic correlation and describe a flow correlation-based identification system architecture which incorporates the four separate methods. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.149-153
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• 2012

We propose a traffic information service for which traffic data are collected over ad-hoc networks from neighbor vehicles, processed to minimize the data size, and eventually provided to its destination. The proposed scheme simply relies on the existing navigtion systems in vehicles and wireless communication devices for vehicle-to-vehicle communication, rather than on a separately established server. It allows collecting and analyzing traffic status of large areas without incorporating separated monitoring systems, e.g., probe cars and enables to provide accurate traffic information to drivers in timely manner. We also evaluate its performance by ns-3 simulation.