• Journal of Digital Contents Society
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• v.16 no.6
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• pp.901-908
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• 2015

Networked multiple CCTV systems are required to effectively trace down long-term abnormal behaviors, such as stalking. However, the occluding event, which often takes place during tracking, may result in critical errors of cessation of tracing, or tracking wrong objects. Thus, utilizing installed regular CCTVs, this study aims to trace the relation tracking in a continuous manner by recognizing distinctive features of each object and its perspective projection depth to address the problem with occluded objects. In addition, this study covers occlusion event between the stationary background objects, such as street lights, or walls, and the targeted object.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.126-128
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• 1998

본 논문에서 구현하고자 하는 웹기반 사용자별 에이전트는 웹을 이용해 정보를 검색하는 사용자들에 대한 사용자 관심도를 사용자의 웹검색 행위를 감시하는 모니터 에이전트에 사용자가 직접 기술하게 하고 이를 별도의 학습서버를 두어 사용자별 프로파일을 만들어 이를 사용자가 확인 및 편집할 수 있게 하였다. 서버에서의 학습 과정은 웹 브라우저를 통하여 수집된 정보를 바탕으로 사용자가 관심을 가지는 웹 문서의 일반적인 내용에 대한 관심 정확도를 높이는 일련의 단어 정제 과정을 통하여 최적의 관심 키워드를 추출하는 작업으로 이루어지며 이는 표현 모델인 사용자 프로파일을 구축하여, 관심 문서를 검색하는데 적절한 정보를 제시하는 것을 목적으로 한다. 이 시스템에서 적용되는 학습 방식은 사용자의 웹 문서 관심도에 의존하므로 웹 문서에 나타나는 텍스트들을 대상으로 C4.5 학습 시스템을 적용한다.

• Journal of Korea Game Society
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• v.16 no.1
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• pp.83-92
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• 2016

An online game is a huge distributed system comprised of servers and untrusted clients. In such circumstances, cheaters may employ abnormal behaviors through client modification or network packet tampering. Client-side detection methods have the merit of distributing the burden to clients but can easily be breached. In the other hand, server-side detection methods are trustworthy but consume tremendous amount of resources. Therefore, this paper proposes a security reinforcement method which involves both the client and the server. This method is expected to provide meaningful security fortification while minimizing server-side stress.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.947-950
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• 2014

The recent APT attacks including cyber terror are caused by a high level of malicious codes and hacking techniques. This implies that essentially, advanced security management is required, from the perspective of 5A. The changes of IT environment are represented by Mobile, Cloud and BYOD. In this situation, the security model needs to be changed, too into the Airport model which emphasizes prevention, and connection, security and integration of functions from the existing Castle model. This study suggested an application method of the risk-based Airport model to the cyber security environment.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.295-310
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• 2014

DCS (Distributed Control System), the main control system of power plants, is an automated system for enhancing operational efficiency by monitoring, tuning and real-time operation. DCS is becoming more intelligent and open systems as Information technology are evolving. In addition, there are a large amount of investment to enable proactive facility management, maintenance and risk management through the predictive diagnostics. However, new upcoming weaponized malware, such as Stuxnet designed for disrupting industrial control system(ICS), become new threat to the main control system of the power plant. Even though these systems are not connected with any other outside network. The main control systems used in the power plant usually have been used for more than 10 years. Also, this system requires the extremely high availability (rapid recovery and low failure frequency). Therefore, installing updates including security patches is not easy. Even more, in some cases, installing security updates can break the warranty by the vendor's policy. If DCS is exposed a potential vulnerability, serious concerns are to be expected. In this paper, we conduct the penetration test by using NESSUS, a general-purpose vulnerability scanner under the simulated environment configured with the Ovation version 1.5. From this result, we suggest a log analysis method to detect the security infringement and react the incident effectively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2006.05a
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• pp.146-150
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• 2006

도축 후 2일째 가열 감량은 시험구(골반골 현수)가 대조구(아킬레스건 현수)에 비하여 유의적으로 감량이 높았으나, 드립량에 있어서는 반대의 결과를 보였으며, 전체 감량은 두 처리구간 차이가 없었다. 연도의 지표인 전단력가는 대조구 $6.45kg/cm^2$로 시험구 $5.52kg/cm^2$에 비하여 유의적으로 높았으며, 골반골 현수를 통하여 도축 후 8일차의 대조구의 전단력 수준을 보여 숙성기간을 약 1주일 정도 단축이 가능하였다. 근절길이는 대조구 $1.93{\mu}m$, 시험구 $2.48{\mu}m$로 시험구가 유의적으로 길어 골반골 현수에 의한 단축 억제 효과로 볼 수 있다. 관능 평가 결과 도축후 2일차에는 대조구에 비하여 시험구가 다즙성, 연도, 향미, 기호성에서 유의적으로 높은 값을 보였으나, 도축 후 8일째에는 대조구와 시험구간에 차이를 보이지 않았다. 출하 연령과 현수방법에 따른 대퇴이두근의 물리적 특성을 비교한 결과로서 근절길이는 모든 월령에서 시험구가 대조구에 비하여 길었다. 전단력은 시험구가 대조구에 비하여 유의적으로 낮은 결과를 보였다. 골반골 현수에 의한 전단력가 감소 효과는 출하 월령이 많을수록 효과가 큰 것으로 나타났다. 드립 발생율은 대조구가 시험구에 비하여 유의적으로 높은 경향을 보였다. 가열 감량과 보수성은 처리구에 관계없이 유의적인 차이를 보이지 않았다. 관능 특성을 비교한 결과 30개월 미만의 경우에는 유의적인 차이를 보이지 않았으나, 30개월 이상에서는 처리구가 대조구에 비하여 다즙성 연도, 향미, 기호성 모두 우수한 것으로 나타났다. 특히 골반골 현수는 출하 월령이 증가됨에 따라 관능 특성의 개선율이 높은 것으로 나타났다. 수 있다.분별을 성공적으로 수행하였다.(p<0.05), 맛, 연도, 다즙성 및 전체적인 기호성은 유의한 차이가 없었다.자체를 악하다고 볼 수 없고 더구나 구원을 이 세상에서의 이탈로 볼 수 없다. 진정한 구원이란 원래 하나님이 보시기에 아름다웠던 그 세상으로의 회복을 포함한다. 이런 면에서 하나님 주권 신앙 하에서 구원이란 전 인격적인 구원, 전 우주적인 구원이 된다. 그렇기 때문에 성도는 세상의 삶과 학문, 예술, 정치, 경제, 사회를 포함한 모든 분야를 하나님의 뜻 가운데서 그 원래의 목적에 부합할 수 있도록 회복시키는 일에 적극 참여해야 한다.자체가 이를 주도하기는 사실 어려움이 있다. 그리고 대형유통점이 영업행위를 영업시간제한에서부터 출점제한에 이르기까지 규제하는 건은 심사숙고하여야 한다. 대형유통점이 국가경제 및 지역사회에 미치는 영향이 부정적인가 긍정적인가에 대해 국내외 학계와 업계에서 여전히 많은 논란이 있기 때문이다. 정부와 지자체에 의한 시장개입은 반드시 필요한 경우에 한해 합당한 방법에 의해 이루어져야 한다. 대형유통점에 대한 규제는 지역사회에 미치는 영향을 다면적으로 평가한 결과에 근거하여 이루어져야 할 것이다. 대부분의 지자체는 체계적인 평가시스템과 객관적인 통계 자료를 갖고 있지 못한 실정이다. 향후 가장 시급한 과제는 시장개방 이후 지난 10년간 대형유통점이 지역사회에 미친 영향에 관한 광범위한 통계자료를 수집하고 이를 체계적으로 분석하여 정책방향을 올바르게 설정하는 것이라 할 수 있다.i와 K. pneumoniae가 존재하며 확산 중임을 시사한다. 앞으로 CTX-M형 ESBL의 만연과 변종 CTX-M형 ESBL의 출연을 감시하기 위한 정기적인 연구와 조사가 필요한 것으로 생각한다., A2-1, B1-1, B2-1의 경우,