• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.6D
• /
• pp.685-691
• /
• 2009

The purpose of this paper is to classify the traffic condition in freeway by the impacts of road weather. The factor analysis showed that weather factors, which are considered as influential, are identified as weather condition (rain or clear), temperature and sight distance with RWIS and VDS data in Seohae bridge used. The result of ANOVA shows that weather is dividedinto clear and rainy; temperature into below and equal or above $5^{\circ}C$ and sight distance into below or equal or above 10km. Based on those factors, the freeway traffic condition has been classified as five different types. The flow-speed model for each traffic conditions was proposed, which was not significant due to the lack of smaple data. Although not sufficient, the methodology to categorize traffic situation model presented in this paper may shed light on the idea for the future and can be used for proper traffic management for each weather condition.

• KIPS Transactions on Computer and Communication Systems
• /
• v.1 no.1
• /
• pp.55-60
• /
• 2012

Recent botnets are widely using the DNS services at the connection of C&C server in order to evade botnet's detection. It is necessary to study on DNS analysis in order to counteract anomaly-based technique using the DNS. This paper studies collection of DNS traffic for experimental data and supervised learning for DNS traffic-based malicious domain classification such as query of domain name corresponding to C&C server from zombies. Especially, this paper would aim to determine significant features of DNS-based classification system for malicious domain extraction by the Principal Component Analysis(PCA).

• Journal of Korean Society of Transportation
• /
• v.24 no.1 s.87
• /
• pp.39-52
• /
• 2006

This study divided the large-scale traffic accident locations into its own characteristics by using Cluster Analysis. Also, Quantification II and Classification and Regression Tree methods were used enabling evaluation for the amount of affecting influence by the crash type. After these analyses, we tested the fitness of the results and suggested the simplification of the quantification index. With the results from the discussed procedure, obvious differences were observed by groups according to the characteristics of crash type from the Discrimination and Classification analysis of divided four groups. Thus, measures and supplementary measures for the traffic accidents could be suggested in groups systematically. However, a lot of missing values in variables caused a huge loss of data and made this study difficult for more detailed analysis, With this difficulty. recording mandatory log files with a standardized format is also recommended to Prevent this Problem in advance.

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.103-115
• /
• 2007

The vulnerability issue on IEEE 802.1x wireless LAN has been permits the malicious attack such as Auth/Deauth flooding more serious rather than we expected. Attacker can generate huge volume of malicious traffic as the same methods on existing wired network. The objective of wireless IP Traceback is to determine the real attack sources, as well as the full path taken by the wireless attack packets. Existing IP Traceback methods can be categorized as proactive or reactive tracing. But, these existing schemes did not provide enhanced performance against DoS attack on wireless traffic. In this paper, we propose a 'TTL based advanced Packet Marking' mechanism for wireless IP Packet Traceback with wireless Classification function. Proposed mechanism can detect and control DoS traffic on AP and can generate marked packet for reconstructing on the real path from the non-spoofed wireless attack source, by which we can construct secure wireless network based on AP with enhance traceback performance.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.3
• /
• pp.45-54
• /
• 2014

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcome the limitations of the existing methods. The first step is a signature-based classifier at the packet-level. The second step consists of pattern heuristic rules and a statistics-based classifier at the flow-level. With pattern heuristic rules, the accuracy can be improved and the amount of traffic to be classified by statistics-based classifier can be reduced. Based on the analysis of different decision tree algorithms, the statistics-based classifier is implemented with REPTree. In addition, the ensemble algorithm is used to improve the performance of statistics-based classifier Through the verification with the real datasets, it is shown that our hybrid scheme provides higher accuracy and lower overhead compared to other existing schemes.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.1
• /
• pp.98-107
• /
• 2017

In these days, the increase of applications that highly use network resources has revealed the limitations of the current research phase from the traffic classification for network management. Various researches have been conducted to solutions for such limitations. The representative study is automatic finding of the common pattern of traffic. However, since the study of automatic signature generation is a semi-automatic system, users should collect the traffic. Therefore, these limitations cause problems in the traffic collection step leading to untrusted accuracy of the signature verification process because it does not contain any of the generated signature. In this paper, we propose an automated traffic collection, signature management, signature generation and signature verification process to overcome the limitations of the automatic signature update system. By applying the proposed method in the campus network, actual traffic signatures maintained the completeness with no false-positive.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.2
• /
• pp.93-104
• /
• 2023

Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.3
• /
• pp.1-11
• /
• 2010

Traffic broadcasting is using a usual traffic information announcement when giving its information to users on the road and for the provision of information useful to drivers, a clear criteria of how to judge with information from informers needs to be established from the perspective of users. In this study, to give some available criteria for current announcement which often causes confusion, cognitive characteristics were investigated and analyzed based on judgment criteria which are commonly felt by correspondents, participants in traffic broadcasting and drivers. The result requires the provision of information that is relied on an average speed where drivers feel little cognitive difference and found a classification where a smooth traffic flow is more than 60km/h, going slow 40~60km/h and congested state less than 40km/h respectively. And from the study of 35 traffic information announcement for different traffic situations, 8 cases of smooth state and 9 cases of congested state were clearly classified but the rest 18 cases of comment were ambiguously perceived by drivers and which requires the necessity of a announcement that uses directly the word of 'smooth', 'slow', and 'congestion' in the actual expression of slow driving. The future study should be focused on the establishment of more definite criteria by representation of nearly real traffic flow, provision of traffic information announcement and the analysis of cognitive response through car dynamic simulators and the kinds.

• Journal of Korean Society of Transportation
• /
• v.14 no.4
• /
• pp.49-76
• /
• 1996

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithms, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog Digital Converted data collect form ILD, this study used a typical and modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method, As a result, the hybrid model of GLVQ neural network model is superior to the other methods.