• Annual Conference of KIPS
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• 2012.04a
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• pp.1436-1438
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• 2012

위치 기반 서비스를 이용하여 미국 지질 조사소에서 지진 정보를 실시간으로 받아와 받아온 정보를 XML 파싱 방법을 이용하여 분석된 정보들을 서버에 있는 데이터베이스에 저장한다. 클라이언트가 값 요청 시 서버에 저장된 정보들을 Socket 통신방식으로 여러 사용자들이 접근 가능하도록 서버를 구현하였다. 클라이언트는 이 정보들을 Overlay를 이용하여 지도에 볼 수 있도록 표시해준다. 이 연구로 인해 지진이 일어날 경우 경고를 이용하여 재산피해를 줄일 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.97-104
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• 2019

Distributed mobility management (DMM) does not use a centralized device. Its mobility functions are distributed among routers; therefore, the mobility services are not limited to the performance and reliability of specific mobility management equipment. The DMM scheme has been studied as a partially distributed architecture, which distributes only a packet delivery domain in combination with the software defined network (SDN) technology that separates the packet delivery and control areas. Particularly, a separated control area is advantageous in introducing a new service, thereby optimizing the network by recognizing the entire network situation and taking an optimal decision. The SDN-based mobility management scheme is studied as a method to optimize the packet delivery path whenever a mobile node moves; however, it results in excessive signaling processing cost. To reduce the high signaling cost, we propose a hybrid distributed mobility management method and analyze its performance mathematically.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.29-35
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• 2019

In this paper, the effects of educational contents on job behavior, the effects of job behavior on job effectiveness and the effects of educational contents on job effectiveness were studied when working in the Software field. For this purpose, a questionnaire survey was conducted on the learning workers who conducted the training in the IT field, and 302 valid questionnaires were used for the analysis. The research model was set up to test exploratory factor and confirmatory factor analysis and hypothesis, and the research hypothesis was tested by applying structural equation. The effects of job behavior on job effectiveness were positively related to job satisfaction, customer orientation, and organizational commitment.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1956-1959
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• 2021

AIS(Automatic Identification System) provides navigational information including identification, position, a ship's course and status to ground and other vessels. To obtain AIS Marine Equipment Approval Service, various requirements are required and meet the requirements International Standards. However, most of the requirements are to identify essential functions, response time, hardware requirements, and communication protocols of AIS. The requirements for the quality of SW are not sufficient or detailed, and the weight is relatively low. As role of SW grows and types become more diverse, AIS SW quality inspection is essential. In this paper, We apply eight-quality characteristics of ISO/IEC 25023 standard to improve SW coverage quality of AIS. Suggest additional AIS SW requirements based on the eight quality characteristics of ISO/IEC 25023 standard.

• Earthquakes and Structures
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• v.22 no.2
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• pp.137-145
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• 2022

As steel is highly sensitive to temperature variations, fire exposure is more destructive in the case of steel structures in comparison to the concrete ones. The performance of an intermediate three-story steel moment frame with 4 spans was studied under the service load, thermal load and post-earthquake fire in this paper. Also, the effects of passive fire-protection materials such as ordinary cement-based and fire-retardant coatings were investigated. To model and analyze the structure; Abaqus software is utilized. In order to apply the earthquake effect, the push-over analysis method is employed. Changes in the stories deflection, endurance time and growth of nonlinear regions due to losses in the steel stiffness and strength, are among the issues considered in this study. As an interesting finding, the beams protected by ordinary cement-based coating could sustain the fire exposure at least for 30 minutes in all cases. The mentioned time is increased by employing a new fire-retardant protection, which could prevent significant loss in the structure resistance against fire, even after 60 minutes of exposure to fire.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.303-309
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• 2022

Software Defined Networking (SDN) is a novel approach that have accelerated the development of numerous technologies such as policy-based access control, network virtualization, and others. It allows to boost network architectural flexibility and expedite the return on investment. However, this increases the system's complexity, necessitating the expenditure of dollars to assure the system's security. Network Function Virtualization (NFV) opens up new possibilities for network engineers, but it also raises security concerns. A number of Internet service providers and network equipment manufacturers are grappling with the difficulty of developing and characterizing NFVs and related technologies. Through Moodle's efforts to maintain security, this paper presents a detailed review of security-related challenges in software-defined networks and network virtualization services.

• Annual Conference of KIPS
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• 2010.04a
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• pp.993-996
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• 2010

우리나라의 전자정부 서비스 대부분은 웹 환경을 기반으로 하고 있으며, 이는 국민생활의 질적인 향상을 가져온 반면 개인정보의 수집, 활용, 유통이 급격히 증가되면서 각종 프라이버시침해, 사이버 위협 등의 부작용이 수반되고 있으며 웹 브라우저의 다양화에 따른 웹접근성이 해결되어야 하고, 장애우 차별을 금지하기 위한 서비스가 동시에 제공되어야 한다. 따라서 웹서비스를 대상으로 하는 사이버 위협에 대한 보안대책과 다양한 서비스에 대한 대책이 중요한 요소로 부각되고 있다. 웹 환경에서의 보안문제를 해결하기 위해 기존에는 방어벽등 보안 모듈 부분을 강화하는데 초점을 맞추었으나, 본 논문에서는 그에 국한되지 않고 소프트웨어 개발초기 단계인 분석, 설계단계에서부터 보안문제를 고려하여 운영단계에 이르기까지 보안 취약점을 해결할 수 있는 방안을 제시하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.501-502
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• 2022

최근 작성한 일기를 SNS에 올려 평범한 사람들이 음악, 음식, 사건 등 소소한 일상을 남기고 우울증 투병기를 공유하여 힘을 얻기도 하는 등 누가 시키지 않아도 일기를 작성하고 간직하는 사람들이 증가하고 있다. 이러한 변화로 일기는 하루의 일상을 기록하는 목적을 넘어 어떤 감정을 느꼈는지 알아차리고 자아를 성찰 및 탐구하는 단계로 발전하고 있다. 그러나 스스로 일기의 키워드를 분석하고 감정이 어떠한지 정확하게 아는 것은 어렵다. 이에 따라 본 논문에서는 제시한 문제를 해결하기 위한 방법으로 KoBERT와 KoNLPy를 활용한 키워드 및 감정분석 일기 서비스를 제안하였다. 본 연구의 키워드 및 감정분석 일기 서비스는 사용자가 무의식적으로 표현하는 텍스트 기반의 일기에서 자주 반복되는 키워드와 감정을 제공하여 자신의 감정상태를 쉽게 인지하고 되돌아볼 수 있도록 제작하였다.