• Journal of the Institute of Electronics and Information Engineers
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• 제51권2호
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.587-590
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• 2004

In this paper proposed a HMM(Hidden Martov Model)-based segmentation method which is able to model shadows as well as foreground and background regions. Shadow of moving objects often obstruct visual tracking. We propose an HMM-based segmentation method which classifies in real time oath objects. In the case of traffic monitoring movies, the effectiveness of the proposed method has been proven through experimental results

• KIPS Transactions on Software and Data Engineering
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• 제12권2호
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Journal of KIISE:Computer Systems and Theory
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• 제30권11호
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Journal of Information Technology Services
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• 제22권5호
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Journal of the Korea Computer Industry Society
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• 제2권12호
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• pp.1505-1516
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• 2001

The goal of Medical multimedia server is to develop computer hardware and software which would enable electronic access, storage, transmission, and display of patient data and images. Since the current network only provides so called "best-effort" services, it is impossible to satisfy QoS guarantee that is required for real time application services for emergency room, operating room etc. Accordingly, world-wide research is being made for a variety of services to provide QoS. he goal of DiffServ is to offer scalable differentiated service in Internet which are made possible by traffic classification and conditioning only performed at an edge(or a boundary) node. In case DiffServ was deployed in the Medical multimedia network, it is difficult to estimate how the QoS mechanism would affect totally the network performance. Therefore, we need to verify by simulation the design of algorithm which provide a variety of differentiated services. In QoS for Medical multimedia network, a simulator is designed and implemented using OPNET to investigate the performance of DiffServ QoS support mechanism. The developed DiffServ simulator may generate packets according to random, and bursty traffic models in order to incorporate diverse traffic conditions in the Medical multimedia network environment. Based on our simulation results, we confirmed that service differentiation is possible by using the EF(Expedited Forwarding) class in DiffServ networks.

• Convergence Security Journal
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• 제7권2호
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• pp.39-47
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• 2007

The scheduling algorithms to provide quality of service (QoS) in broadband convergence network (BcN) are compared and analysed. The main QoS management methods such as traffic classification, traffic processing in the input queue and weighted queueing are first analysed, and then the major scheduling algorithms of round robin, priority and weighted round robin under recently considering for BcN to supply real time multimedia communications are analysed. The simulation results by NS-2 show that the scheduling algorithm with proper weights for each traffic class outperforms the priority algorithm.

• The Journal of the Korea Contents Association
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• 제8권6호
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• pp.54-65
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• 2008

When DDoS attack is achieved, malicious host discovering is more difficult on wireless network than existing wired network environment. Specially, because wireless network is weak on wireless user authentication attack and packet spoofing attack, advanced technology should be studied in reply. Integrated Access Device (IAD) that support VoIP communication facility etc with wireless routing function recently is developed and is distributed widely. IAD is alternating facility that is offered in existent AP. Therefore, advanced traffic classification function and real time attack detection function should be offered in IAD on wireless network environment. System that is presented in this research collects client information of wireless network that connect to IAD using AirSensor. And proposed mechanism also offers function that collects the wireless client's attack packet to monitoring its legality. Also the proposed mechanism classifies and detect the attack packet with W-TMS system that was received to IAD. As a result, it was possible for us to use IAD on wireless network service stably.

• Journal of KIISE:Software and Applications
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• 제34권10호
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• pp.929-936
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• 2007

The novel vision-based scheme for real-time extracting traffic parameters is proposed in this paper. Detecting and tracking of vehicle is processed at local region installed by operator. Local region is divided to segmented regions by edge and frame difference, and the segmented regions are classified into vehicle, road, shadow and headlight by statistical and geometrical features. Vehicle is detected by the result of the classification. Traffic parameters such as velocity, length, occupancy and distance are estimated by tracking using template matching at local region. Because background image are not used, it is possible to utilize under various conditions such as weather, time slots and locations. It is performed well with 90.16% detection rate in various databases. If direction, angle and iris are fitted to operating conditions, we are looking forward to using as the core of traffic monitoring systems.