• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.991-1000
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• 2007

A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.371-379
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• 2022

Darknet is based on the characteristics of anonymity and security, and this leads darknet to be continuously abused for various crimes and illegal activities. Therefore, it is very important to detect and classify darknet traffic to prevent the misuse and abuse of darknet. This work proposes a novel approach, which uses the Gradient Boosting techniques for darknet traffic detection and classification. XGBoost and LightGBM algorithm achieve detection accuracy of 99.99%, and classification accuracy of over 99%, which could get more than 3% higher detection accuracy and over 13% higher classification accuracy, compared to the previous research. In particular, LightGBM algorithm could detect and classify darknet traffic in a way that is superior to XGBoost by reducing the learning time by about 1.6 times and hyperparameter tuning time by more than 10 times.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.79-85
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• 2006

Generally, it is essential that high-speed routers, switches, and network security appliances should have an efficient packet classification scheme in order to achieve the high-speed packet forwarding capability. For the multi-gigabit packet-processing network equipment the high-speed content search hardware such as TCAM and search engine is recently used to support the content-based packet inspection. During the packet classification process, hundreds and thousands of rules are applied to provide the network security policies regarding traffic screening, traffic monitoring, and traffic shaping. In addition, these rules could be dynamically changed during operations of systems if anomaly traffic patterns would vary. Particularly, in the high-speed network, an efficient algorithm that updates and reorganizes the packet classification rules is critical so as not to degrade the performance of the network device. In this paper, we have proposed an efficient update algorithm using a partial-ordering that can relocate the dynamically changing rules at the TCAM. Experimental results should that our algorithm does not need to relocate existing rules feature until 70$\%$ of TCAM utilization.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• IE interfaces
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• v.18 no.3
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• pp.333-342
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• 2005

In this paper, we propose an efficient forecasting methodology of the mid and long-term frequency demand in Korea. The methodology consists of the following three steps: classification of basic service group, calculation of effective traffic, and frequency forecasting. Based on the previous studies, we classify the services into wide area mobile, short range radio, fixed wireless access and digital video broadcasting in the step of the classification of basic service group. For the calculation of effective traffic, we use the measures of erlang and bps. The step of the calculation of effective traffic classifies the user and basic application, and evaluates the effective traffic. Finally, in the step of frequency forecasting, different methodology will be proposed for each service group and its applications are presented.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.354-362
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• 2000

Increasing amount of road tragic in 90's has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving conditions based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we apply several data fusion, ensemble and clustering algorithms in an effort to increase the accuracy of individual classifiers for the accident severity. An empirical study results indicated that clustering works best for road traffic accident classification in Korea.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.39-52
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• 2002

Traffic data by vehicle classification is difficult for mutual exchange of data due to the different vehicle classification from each other by the data sources; as a result, application of the data is very limited. In Particular. in case of TCS vehicle classification in national highways, passenger car, van and truck are mixed in one category and the practical usage is very low. The research standardize the vehicle classification to convert other data and develop the model which can estimate national highway traffic data by the standardized vehicle classification from the raw traffic data obtained at the highway tollgates. The tollgates are categorized into several groups by their features and the model estimates traffic data by the standardized vehicle classification by using the point estimation and bootstrap algorithm. The result indicates that both of the two methods above have the significant level. When considering the bias of the extreme value by the sample size, the bootstrap algorithm is more sophisticated. Using result of this study, we is expect the usage improvement of TCS data and more specific comparison between the freeway traffic investigation and link volume on freeway using the TCS data.

• Journal of the Korean Institute of Navigation
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• v.20 no.2
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• pp.51-58
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• 1996

For the smooth flow of traffic vehicles and its effective management, it is necessary to have an exact information on traffic condition, i.e., the volume of traffic, velocity, occupancy and classification of vehicles. In particular, for classification of vehicles, there has been only image processing method using camera, where the method can obtain much information but rather expensive. In this paper, an algorithm for the measurement of velocity and total length of vehicles has been proposed to develop a general traffic management system, which is necessary to discriminate the class of vehicles. In order to realize the proposed algorithm, we have developed an ultrasonic spatial filtering method, which has better performance than that of using the traditional vehicle detector. To have this system to be constructed, we have introduced three sets of ultrasonic devices where each has one transmitter and two receivers which are arranged to obtain the spatial difference of objects. The velocity of vehicles can be measured by analyzing the occurrence time of pulses and their time differences. The total length of vehicles can be given by multiplying velocity with time interval of pulses sequence. To confirm the effectiveness of this measuring system, the experiment by the spatial filtering method using the ultrasonic sensors has been carried out. As the results, it is found that the proposed method can be used as one of measurement tools in the general traffic management system.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.169-172
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• 2000

IP Switching is a new routing technology proposed to improve the performance of IP routers. Flow classification is one of the key issues in IP Switching. To achieve better performance, flow classification should be matched to the varying IP traffic and an IP switch should make use of its hardware switching resources as fully as possible. This paper proposes an adaptive flow classification algorithm for IP Switching. By dynamically adjusting the values of its control parameters in response to the present usage of the hardware switching resources, this adaptive algorithm can efficiently match the varying IP traffic and thus improve the performance of an IP switch.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1077-1080
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• 2003

본 연구는 룰들의 각 필드들을 index하여 곱한 cross-product 테이블을 이용한 packet classification 알고리즘에 대해 연구하고 그 것의 성능을 평가하고 분석한다. 현재 Packet Classification은 Packet Filtering, Policy Routing, Accounting & Billing, Traffic Rate Limiting, Traffic Shaping, 등등의 서비스를 위한 가장 핵심적인 작업이다. 그러나 이들을 빠르게 서비스하는 알고리즘은 아직 존재하지 않는다. 단지 하드웨어 TCAM 을 이용해서 작은 룰들에 대한 처리만이 어느 정도 가능한 실정이다. 이에 본 연구는 소프트웨어를 이용한 cross-product 알고리즘의 효용성을 가늠하고자 연구하고 이를 실제 구현해 평가하고자 한다.