• Convergence Security Journal
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• v.17 no.1
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• pp.31-38
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• 2017

As the cyber-attacks through the networks advance, it is difficult for the intrusion detection system based on the simple rules to detect the novel type of attacks such as Advanced Persistent Threat(APT) attack. At present, many types of research have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to satisfy the both requirements: accuracy, and fast response. The comparison between the proposing approach and previously proposed other approaches is conducted against NSL_KDD data set for the evaluation of the performance of the proposing approach.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.425-430
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• 2015

The Sensor Registry System(SRS) provides sensor metadata to a user for instant use and seamless interpretation of sensor data in a heterogeneous sensor network environment. The existing sensor registry system cannot provide sensor metadata in case that the network connection is not available or is unstable. To resolve the problem, this paper proposes an extension of sensor registry system using network coverage information. The extended system sends a set of sensor metadata to the user by using network coverage open data (mobile vendors, signal strength, communication type). The extended SRS proposed in this paper supports a safer sensor metadata provision than the existing SRS, and it thus improves the quality of application services.

• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.296-307
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• 2021

A digital hologram (DH) is an ultra-high value-added video content that includes 3D information in 2D data. Therefore, its intellectual property rights must be protected for its distribution. For this, this paper proposes a watermarking method of DH using a deep neural network. This method is a watermark (WM) invisibility, attack robustness, and blind watermarking method that does not use host information in WM extraction. The proposed network consists of four sub-networks: pre-processing for each of the host and WM, WM embedding watermark, and WM extracting watermark. This network expand the WM data to the host instead of shrinking host data to WM and concatenate it to the host to insert the WM by considering the characteristics of a DH having a strong high frequency component. In addition, in the training of this network, the difference in performance according to the data distribution property of DH is identified, and a method of selecting a training data set with the best performance in all types of DH is presented. The proposed method is tested for various types and strengths of attacks to show its performance. It also shows that this method has high practicality as it operates independently of the resolution of the host DH and WM data.

• Database Research
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• v.34 no.3
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• pp.124-136
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• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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• pp.69-78
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• 2023

In this paper, IO+5G dedicated hardware is developed and an AI device communication system equipped with a 5G is designed and tested. The AI device communication system equipped with a 5G receives the collected real-time images and the information collected from the IoT sensor in real time is to analyze the information and generates the risk detection events in the AI processing board. The event generated in the AI processing board creates a 5G channel in the dedicated hardware equipped with IO+5G. The created 5G channel delivers event video to the control video server. The 5G based dongle network enables faster data collection and more precise data measurement compared to wireless LAN and 5G routers. As a result of the experiment in this paper, the average test result of the 5G dongle network is about 51% faster than the Wi-Fi average test result in downlink and about 40% faster in uplink. In addition, when comparing the test result with terms of the 5G rounter to be set to 80% upload and 20% download, the average test result is that the 5G dongle network is about 11.27% faster when downloading and about 17.93% faster when uploading. when comparing the test result with terms of the the router to be set to 60% upload and 40% download, the 5G dongle network is about 11.19% faster when downlinking and about 13.61% faster when uplinking. Therefore, in this paper it describes that the developed 5G dongle network can improve the results by collecting data and analyzing it faster than wireless LAN and 5G routers.

• Korea Journal of Hospital Management
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• v.23 no.2
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• pp.28-41
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• 2018

Purpose: This study purposed to analyze the spatial accessibility of mental health institutions in Ganwon-Do using Geographic Information System and to suggest policy implications. Methodology: Network analysis was applied to assess the spatial accessibility of mental health institutions in Gangwon-Do. To perform the network analysis, network data set was built using administrative district map, road network, address of mental health institutions in Gangwon-Do. After building network data set, Two network analysis methods, 1) Service area analysis, 2) Origin Destination cost matrix were applied. Service area analysis calculated accessive areas that were within specified time. And using Origin Destination cost matrix, travel time and road travel distance were calculated between centroids of Eup, Myeon, Dong and the nearest mental health institutions. Result: After the service area analysis, it is estimated that 19.63% of the total areas in Gangwon-Do takes more than 60 minutes to get to clinic institutions. For hospital institutions, 23.08% of the total areas takes more than 60 minutes to get there. And 59.96% of Gangwon-do takes more than 30 minutes to get to general hospitals. The result of Origin-Destination cost matrix showed that most Eup Myeon Dong in Gangwon-Do was connected to the institutions in Wonju-si, Chuncheon-si, Gangneung-si. And it showed that there were large regional variation in time and distance to reach the institutions. Implication: Results showed that there were regional variations of spatial accessibility to the mental health institutions in Gangwon-Do. To solve this problem, Several policy interventions could be applied such as mental health resources allocation plan, telemedicine, providing more closely coordinated services between mental health institutions and community mental health centers to enhance the accessibility.

• Convergence Security Journal
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• v.20 no.4
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• pp.71-80
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• 2020

In order to provide realistic traffic to the cyber warfare training system, it is necessary to prepare a traffic distribution plan in advance and to create a training data set using normal/threat data sets. This paper presents the design and implementation results of a method for creating a traffic distribution plan and a training data set to provide background traffic like a real environment to a cyber warfare training system. We propose a method of a traffic distribution plan by using the network topology of the training environment to distribute traffic and the traffic attribute information collected in real and simulated environments. We propose a method of generating a training data set according to a traffic distribution plan using a unit traffic and a mixed traffic method using the ratio of the protocol. Using the implemented tool, a traffic distribution plan was created, and the training data set creation result according to the distribution plan was confirmed.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.170-170
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• 2020

Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.491-498
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• 2003

The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using FCM(Fuzzy Cognitive Maps) that can detect intrusion by the DoS attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The SPuF(Syn flooding Preventer using Fussy cognitive maps) model captures and analyzes the packet informations to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance comparison, the "KDD′99 Competition Data Set" made by MIT Lincoln Labs was used. The result of simulating the "KDD′99 Competition Data Set" in the SPuF model shows that the probe detection rates were over 97 percentages.