• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.19-24
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• 1999

This paper addresses a new project being carried out at Satellite Technology Research Center. The purpose of the project is to implement a system which receives, processes and stores 1m resolution satellite image transmitted at over 300Mbps down link data rates. In order to develop such a system, a system operational concept design and a requirements study were being carried out As a result of the operational concept design, system objectives, system context and system functions were defined. The system shall be operated according to the philosophy of maximum automation. rapid processing, reliability, integrity, cost effectiveness, and expandability. The system is divided into twelve independent processes and its behavior is modeled by operational scenario, which are combinations of independent processes. Process information and logs generated by the system shall be stored in databases and data received and generated be automatically archived and managed in a hierarchical storage device. The system shall have redundant components in order to be ready for recovering from sudden system failures. This paper will describe in detail the system operational concept design and the system requirements derived from the operational concept design.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.357-362
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• 2009

휴대 전화를 이용해 전화를 거는 다양한 방법이 있지만 사용자의 통화 내역을 바탕으로 시간적인 다양한 정보를 활용하여 가장 통화할만한 상대를 추천해주는 방식은 이용되고 있지 않다. 사용자가 휴대전화로 언제 누구에게 전화를 걸었는지 알려주는 통화내역은 휴대전화가 알아낼 수 있는 다양한 상황 정보와 결합되어 사용자의 통화 스타일이나 패턴을 알려줄 수 있는 유용한 데이터로써 상황에 맞는 적절한 통화의 추천에 이용될 수 있다. 이 논문에서는 실제 통화 기록을 바탕으로 한 통화 추천 방법의 디자인 과정과 추천 알고리즘 검증 과정, 실제 사용자들을 대상으로 그 추천 목록을 테스트한 결과를 제시한다.

• 한국정보처리학회논문지
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• 제6권11S호
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• pp.3410-3419
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• 1999

Autonomous Intrusion Analysis Agent(AIAA) is Incident Response Team staff's tool that scans, analyses, reports and alerts the traces of intrusion based on system logs and intruder's backdoors inside compromised system by IR staff after security incident is reported to the IR team. AIAA is intelligent to recognize to check out who is intruder from all the user accounts and to report the suspected candidates to the master control system in IR team. IR staff who controls AIAA with master system can pick up an intruder from the candidates reported by AIAA agent and review all related summary reports and details including source host's mane, finger information, all illegal behavior and so on. AIAA is moved to compromised system by the staff to investigate the signature of intrusion along the trace of victim hosts and it is also operated in secret mode to detect the further intrusion. AIAA is alive in all victim systems until the incident is closed and IR staff can control AIAA operation and dialogue with AIAA agent in Web interface.

• 한국빅데이터학회지
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• 제2권2호
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• pp.105-114
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• 2017

오늘날 많은 서비스 지원이 웹 사이트를 통해 제공되고 있다. 웹 사이트의 이용 및 효율성을 최적화하기 위하여 방문자들의 이용 패턴 분석이 매우 중요하다. 본 연구에서는 BPI Challenge 2016에서 제공하는 웹 사이트 접속 로그를 분석하여 이용 패턴 분석 및 이용자 그룹별 비교 분석 연구를 수행하였다. 이 데이터는 네덜란드 고용보험사(UWV)의 IT 시스템의 웹 사이트 접속 로그를 제공하며, 고객의 인적 정보, 해당 기관의 웹 사이트를 사용할 때의 고객 행동을 설명하는 클릭 데이터 등의 대한 정보를 포함하고 있다. 본 연구에서는 프로세스 마이닝 기법을 이용하여 사용 고객의 사용 패턴과 고객 그룹 간 특징을 분석하여, 궁극적으로 웹 서비스를 이용하는 고객들을 대상으로 서비스 품질을 향상시키고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• 한국지역사회생활과학회지
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• 제16권1호
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• pp.3-14
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• 2005

The importance of time grows greater these days. For that reason, much research has been conducted to investigate time use. In particular, time spent in housework has been a focus in the human ecology. This study aimed to look into the field of research about time spent in housework from 1991 to 2004. To conduct the inquiry, 35 research projects were analyzed by subject. It turned out that most of the research studied the actual state of time use and related variables. Secondly, the research was classified by the measurement method, such as who wrote the time log or questionnaire, what type of response the researcher wanted and what kind of recording method was used. In 30 research projects, respondents wrote in their time logs and questionnaires by themselves. A few research projects presented certain types of behavior as examples of the actions in everyday life. For the recording method, 22 of the projects employed the time log method and 11 used questionnaires. Thirdly, all the research was categorized by the respondent of each project. 14 projects were targeted .at housewives and 14 were for married couples. Lastly, the definition of time spent in housework was used to consider these 35 studies. Most of the research studies defined the 'time spent in housework' as the total amount of the time spent in eating, clothing, housing, family care and household management.

• Nuclear Engineering and Technology
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• 제49권3호
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• pp.484-494
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• 2017

In industrial plants such as nuclear power plants, system operations are performed by embedded controllers orchestrated by Supervisory Control and Data Acquisition (SCADA) software. A targeted attack (also termed a control aware attack) on the controller/SCADA software can lead a control system to operate in an unsafe mode or sometimes to complete shutdown of the plant. Such malware attacks can result in tremendous cost to the organization for recovery, cleanup, and maintenance activity. SCADA systems in operational mode generate huge log files. These files are useful in analysis of the plant behavior and diagnostics during an ongoing attack. However, they are bulky and difficult for manual inspection. Data mining techniques such as least squares approximation and computational methods can be used in the analysis of logs and to take proactive actions when required. This paper explores methodologies and algorithms so as to develop an effective monitoring scheme against control aware cyber attacks. It also explains soft computation techniques such as the computational geometric method and least squares approximation that can be effective in monitor design. This paper provides insights into diagnostic monitoring of its effectiveness by attack simulations on a four-tank model and using computation techniques to diagnose it. Cyber security of instrumentation and control systems used in nuclear power plants is of paramount importance and hence could be a possible target of such applications.

• 정보보호학회논문지
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• 제29권2호
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• pp.321-330
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• 2019

기업에서 내부자에 의한 기업 기밀 유출 방지는 기업의 생존을 위한 필수 과제이다. 내부자에 의한 정보유출 사고를 막기 위해 기업에서는 보안 솔류션을 도입하여 적용하고 있으나 접근 권한이 있는 내부자의 이상행위를 효과적으로 탐지하는 데에는 한계가 있다. 이번 연구에서는 기업의 제품 제조 이력, 품질 정보 등을 담고 있는 제조정보시스템의 작업자 작업화면 접근 로그 데이타를 기계학습 기법의 비지도학습 알고리즘을 활용하여 정상적인 접근 로그와 비정상적인 접근 로그를 효과적으로 군집화하는 방법을 연구하여 이상징후 탐지를 위한 최적화된 속성 선택 모델을 제시하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.295-311
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• 2023

Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

• 융합보안논문지
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• 제22권3호
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• pp.13-18
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• 2022

IT 기술의 급속한 발전과 함께 기존 산업과의 융합을 통해 4차 산업혁명 기술을 기반으로 프로세스의 간소화 및 생산성을 높일 수 있는 스마트 제조가 증가하고 있으며, 이와 비례하여 공급망 공격에 대한 보안위협도 증가하고 있다. 공급망 공격의 경우 사전 탐지가 힘들고 피해 규모가 매우 크다는 점 때문에 차세대 보안 위협으로 부상하고 있으며 이에 따른 탐지 기법에 대한 연구가 필요하다. 따라서 본 논문에서는 오픈소스 기반 분석 솔루션인 ELK Stack과 Sysmon을 통해 다중 환경에서 실시간으로 로그를 수집, 저장, 분석 및 시각화하여 공급망 공격에 대한 이상 행위 등의 정보를 도출하여 효율적인 탐지 기법을 제공하고자 한다.