• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.693-704
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• 2020

As a company's network structure is getting bigger and the number of security system is increasing, it is not easy to quickly detect abnormal traffic from huge amounts of security system events. In this paper, We propose traffic visualization analysis system(FDANT-PCSV) that can detect and analyze security events of information security systems such as firewalls in real time. FDANT-PCSV consists of Parallel Coordinates visualization using five factors(source IP, destination IP, destination port, packet length, processing status) and Sankey visualization using four factors(source IP, destination IP, number of events, data size) among security events. In addition, the use of big data-based SIEM enables real-time detection of network attacks and network failure traffic from the internet and intranet. FDANT-PCSV enables cyber security officers and network administrators to quickly and easily detect network abnormal traffic and respond quickly to network threats.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.17-20
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• 2018

대중의 관심인 공공이슈를 파악하기 위하여 다양한 종류의 빅데이터를 분석하는 연구가 진행되고 있다. 그러나 기존의 연구에서는 키워드의 노출 횟수만 파악하여 결과로 반영한다. 본 논문은 포털 사이트로부터 얻은 언론사별 뉴스 빅데이터를 이용하여 키워드별 노출 빈도수, 댓글 수 및 추천 수를 반영한 분석 방법을 제안하였다. 공공이슈를 추출하여 얻어낸 키워드들을 워드클라우드, Sankey다이어그램과 같은 형태로 시각화하여 사용자에게 제공한다. 제안된 방법을 사용하면 대중의 반응을 반영한 분석 결과를 확인 할 수 있다.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.4
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• pp.300-309
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• 2012

Long endurance is a key issue in the application of unmanned aerial vehicles. This study presents feasibility test results when fuel cell system as an alternative to the conventional engine is applied for the power of the UAV after the 150W fuel cell system is developed and packaged to the 1/4 scale super cub airplane. Fuel cell system is operated by dead-end method in the anode part and periodically purged to remove the water droplet in flow field during the operation. Oxygen in the air is supplied to the stack by the two air blowers. And fuel cell stack is water cooled by cooling circuit to dissipate the heat generated during the fuel cell operation. Weight balance is considered to integrate the stack and balance of plant (BOP) in package layout. In flight performance test, we demonstrated 4 times standalone take-off and landing. In the laboratory test simulating the flight condition to quantify the energy flow, the system is analyzed in detail. Sankey diagram shows that electric efficiency of the fuel cell system is 39.2%, heat loss 50.1%, parasitic loss 8.96%, and unreacted purged gas 1.67%, respectively compared to the total hydrogen input energy. Feasibility test results show that fuel cell system is high efficient and appropriate for the power of UAV.

• The Journal of Bigdata
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• v.5 no.2
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• pp.145-153
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• 2020

This study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and is based on artificial intelligence-based HPAI spread analysis and patterning. Highly Pathogenic Avian Influenza (HPAI) is coming from abroad through migratory birds, but it is not clear exactly how it spreads to farms. In addition, it is assumed that the main cause of the spread is the vehicle, but the main cause of the spread is not exactly known. However, it is necessary to analyze the relationship between the vehicles and the facilities at the farms where they occur, as the type of vehicles that visit the farms most frequently is between farms and facilities, such as livestock transportation and feed transportation. In this paper, based on the Korea Animal Health Integrated System (KAHIS) data provided by Animal and Plant Quarantine Agency, the main cause of HPAI virus transfer is to be confirmed between vehicles and facilities.