• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Journal of Communications and Networks
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• 제18권5호
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• pp.818-825
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• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.95.2-95
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• 2002

1. Introduction of Camera Detection system Camera Detection system is an equipment that can detect realtime traffic information by image processing techniques. This information can be used to analyze and control road traffic flow. It is also used as a method to detect and control traffic flow for ITS(Intelligent Transportation System). Traffic information includes speed, head way, traffic flow, occupation time and length of queue. There are many detection systems for traffic data. But video detection system can detect multiple lanes with only one camera and collect various traffic information. So it is thought to be the most efficient method of all detection system. Though the...

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• 대한교통학회지
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• 제34권3호
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• pp.278-290
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• 2016

연속교통류 운영 및 설계에서는 최대통과교통류율에 따른 교통류 상태변화 분석이 중요하다. 최대통과교통류율은 연속교통류 운영상태를 평가함에 있어 기준이 되고 있으며, 병목현상과 같은 지 정체 발생시 최대통과교통류율이 급격히 감소하게 된다. 현재까지 이러한 연속교통류 운영과 관련된 다양한 연구들이 수행되었지만, 변화되는 교통량을 명확하게 식별하지 못하고 있다. 이에 본 연구에서는 교통운영 및 설계 등의 다양한 연구를 수행하는 데 있어 가장 중요한 실시간 교통량 변화 검지 방법론에 대한 연구를 실시한다. 이를 위하여 도시고속도로 자유로 구간의 24시간 레이더검지기의 시계열 자료를 이용하며, 교통류 상태 구분에는 통계적 기법의 일환인 터닝포인트 분석(Tunring Point Analysis, 이하 TPA)를 적용한다. TPA는 베이지안 접근법(bayesian approach)을 이용하며, 차량도착은 포아송 분포로 가정한다. 분석대상 구간에 대한 터닝포인트(Turning Point, 이하 TP)를 도출하였으며, 교통량이 변화되는 시점을 확인할 수 있었다. 또한 실시간 교통상태변화 검지를 위한 방법으로 TP지속시간을 설정하여 분석을 실시하였으며, 실시간으로 교통량의 변화를 검지하였다. 이는 기존의 직관적이고 경험적인 접근법의 한계를 극복할 수 있는 장점을 가지며, 실시간으로 교통량 변화를 식별할 수 있어 램프미터링(ramp-metering), 가변차로 등의 교통운영관리에 적용이 가능하다.

• 한국멀티미디어학회논문지
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• 제18권2호
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• pp.189-198
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• 2015

Despite the fact that traffic engineering techniques have been comprehensively utilized in the past to enhance the performance of communication networks, the distinctive characteristics of Software Defined Networking (SDN) demand new traffic engineering techniques for better traffic control and management. Considering the behavior of traffic, large flows normally carry out transfers of large blocks of data and are naturally packet latency insensitive. However, small flows are often latency-sensitive. Without intelligent traffic engineering, these small flows may be blocked in the same queue behind megabytes of file transfer traffic. So it is very important to identify large flows for different applications. In the scope of this paper, we present an approach to detect large flows in real-time without even a short delay. After the detection of large flows, the next problem is how to control these large flows effectively and prevent network jam. In order to address this issue, we propose an approach in which when the controller is enabled, the large flow is mitigated the moment it hits the predefined threshold value in the control application. This real-time detection, marking, and controlling of large flows will assure an optimize usage of an overall network.

• 한국ITS학회 논문지
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• 제13권4호
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• pp.1-11
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• 2014

도로상에서 발생하는 교통 혼잡비용은 지역 간 도로 보다는 도심부 내에서 비중 있게 발생하며, 이는 전체 혼잡비용의 약 63.39%를 차지하고 있다. 따라서, 교통혼잡비용의 절감을 위해서는 도심부의 교통 혼잡을 해소하는 것이 중요하다. 도심부의 교통 혼잡은 반복정체와 비반복정체로 구분되며, 비반복 정체를 신속하고 정확하게 검지하는 것이 교통혼잡의 해소에 있어 무엇보다 중요하다. 그러나 돌발상황 검지에 관한 연구는 대부분 연속류를 대상으로 수행되어 왔다. 도심부 단속류 도로의 경우, 신호 교차로 주정차 차량 등 다양한 변수가 존재하기 때문에 연속류에 적용되는 돌발상황 검지 알고리즘을 수정없이 적용하기에 무리가 있다. 따라서 본 연구에서는 도심부 단속류 도로를 대상으로 수집된 GPS 기반의 차량궤적 데이터에 인공신경망을 적용하여 돌발상황검지 모형을 구축하였다. 제안된 모형의 정확도 검증 결과, 돌발상황 검지율 46.15%, 오보율 25.00%가 도출되었다. 이러한 결과는 단속류를 대상으로 하는 초기 연구 결과로서 의미가 있다. 또한 내비게이션 장치와 같은 차량 궤적 데이터만을 활용하여 비반복정체를 검지 할 수 있는 가능성을 제시 했다는 것에 의미를 찾을 수 있을 것이다.

• Journal of Communications and Networks
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• 제14권3호
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• 한국정보과학회논문지:정보통신
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• 제37권4호
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• pp.263-271
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• 2010

SIP와 RTP를 기반으로 한 인터넷 전화 서비스가 널리 보급되고 있다. 이와 함께 VoIP 전화연결 지연, 방해, 종료 및 음성 통화 품질 감소 등의 피해를 주는 VoIP 이상 트래픽들이 등장하기 시작했다. 국내 대부분의 VoIP 응용들은 현재 표준으로 정의되어 있는 보안 프로토콜을 사용하지 않고 있어 공격자가 패킷을 쉽게 스니핑하고 사용자의 정보 및 헤더 정보를 얻을 수 있을 뿐만 아니라 이상 트래픽을 쉽게 생성시킬 수 있다. 본 논문에서는 무선랜 상에서 SIP/RTP 패킷 스니핑을 통하여 CANCEL, BYE DoS 및 RTP 플러딩 이상 트래픽의 생성 방법과 플로우 기반 트래픽 모니터링을 통하여 VoIP 응용 이상 트래픽 탐지 방법을 제시한다. 실제 상용 VoIP 망에서 실험한 결과 이들 이상 트래픽을 97% 탐지하였다.