• 산경연구논집
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• 제11권2호
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• pp.33-39
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• 2020

Purpose: The purpose of this study is that victims of technology leaks and people concerned about leaks complain of stress over security concerns. However, there are no psychological treatments among the government's comprehensive plans to prevent technology leaks. Therefore, the government intends to present education methods using the NLP (Neuro Linguistic Program), a collective counseling technique, to heal the psychological injury of the victims. Psychological counseling methods include cognitive behavioral therapy, psychoanalytic behavioral therapy, humanism therapy, art therapy, and other psychological therapies. Among them, NLP (Neuro Linguistic Programming) method was used. NLP has three concepts: neuron, language, and programming, and is used as a general method for group counseling. Research design, data and methodology: In relation to composition, Chapter 1 explained the purpose and necessity of the study, Chapter 2 explained the types of psychological counseling and NLPs to help understand the study, introduced the prior study related to the development of collective counseling programs through NLP, and Chapter 3 developed a security psychological counseling education program. In addition, FGI(Focus Group Interview) was conducted for professionals. Results: Corporate counseling considered most in this study should satisfy client, counselor and manager differently from individual counseling. For this purpose, the result was composed of 11 times. In order to derive personal problems for clients, they consisted of finding, loving, expressing, and emancipating self. And, It solved the leakage anxiety to suggest a professional solution for the counselor. In addition, this course helps them become familiar with counseling techniques for becoming a good security administrator. Lastly, it was configured to leave the result for the manager to suggest the organizational development method through this training. The implication of this study is to derive psychological counseling methods for security officers. Most companies in the field of security counseling complain about technology leakage stress. There is currently no psychotherapy support project under the policy. And It was developed because it can expect sales improvement from security consultation. Conclusions: In conclusion, the results were organized to be left to the manager so that he could suggest how to develop the organization through this time.

• Journal of Radiation Protection and Research
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• 제43권4호
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• pp.154-159
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• 2018

Background: To prevent small leakage accidents, a real-time and direct detection system for small leaks with a detection limit below that of existing systems, e.g. $0.5gpm{\cdot}hr^{-1}$, is required. In this study, a small-size beta detector, which can be installed inside the reactor containment (CT) building and detect small leaks directly, was suggested and its feasibility was evaluated using MCNPX simulation. Materials and Methods: A target nuclide was selected through analysis of radiation from radionuclides in the reactor coolant system (RCS) and the spectrum was obtained via a silicon detector simulated in MCNPX. A window was designed to reduce the background signal caused by other nuclides. The sensitivity of the detector was also estimated, and its shielding designed for installation inside the reactor CT. Results and Discussion: The beta and gamma spectrum of the silicon detector showed a negligible gamma signal but it also contained an undesired peak at 0.22 MeV due to other nuclides, not the $^{16}N$ target nuclide. Window to remove the peak was derived as 0.4 mm for beryllium. The sensitivity of silicon beta detector with a beryllium window of 1.7 mm thickness was derived as $5.172{\times}10^{-6}{\mu}Ci{\cdot}cc^{-1}$. In addition, the specification of the shielding was evaluated through simulations, and the results showed that the integrity of the silicon detector can be maintained with lead shielding of 3 cm (<15 kg). This is a very small amount compared to the specifications of the lead shielding (600 kg) required for installation of $^{16}N$ gamma detector in inside reactor CT, it was determined that beta detector would have a distinct advantage in terms of miniaturization. Conclusion: The feasibility of the beta detector was evaluated for installation inside the reactor CT to detect small leaks below $0.5gpm{\cdot}hr^{-1}$. In future, the design will be optimized on specific data.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• 융합보안논문지
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• 제10권3호
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• pp.23-28
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• 2010

최근 국내 기업의 핵심기술 유출은 해마다 증가하고 있고 국가적인 차원에서 피해액 손실도 매년 크게 증가하고 있다. 최근 5년 간 우리나라 주요 사업의 기술유출에 따른 피해액은 220조 원에 달했고 2010년 총 예산과 비슷한 액수이다. 또한 핵심기술 유출 유형별로는 전직 직원, 현직 직원, 협력 업체 직원, 유치과학자, 투자업체 순으로 내부 인력에 의한 핵심기밀 유출이 가장 많은 것으로 나타났다. 이처럼, 사람에 의해서 핵심기밀 유출이 가장 많이 발생한 것에 따라 기업의 인원 보안 관리에 대한 대책 및 관리가 제대로 이루어 지지 않는 것을 미루어 짐작할 수 있다. 따라서 본 논문은 기업에서의 핵심기술 유출을 방지하기 위하여 내부 인력에 대한 사전 정보를 데이터 마이닝 방법을 통해서 핵심기술 유출 징후 분류 방법을 제안한다.

• 한국융합학회논문지
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• 제10권10호
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• pp.7-13
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• 2019

대부분의 천연가스(NG)는 공기 중으로 누출 되며 그중에서도 메탄가스의 누출은 기후에 많은 영향을 준다. 미국 도시의 거리에서 메탄가스 누출 데이터를 수집하였다. 본 논문은 메탄가스누출 정도를 예측하는 딥러닝(Deep Neural Network)방법을 제안하였으며 제안된 방법은 OrdinalEncoder(OE) 기반 K-means clustering과 Multilayer Perceptron(MLP)을 활용하였다. 15개의 특징을 입력뉴런과 오류역전파 알고리즘을 적용하였다. 데이터는 실제 미국의 거리에서 누출되는 메탄가스농도 오픈데이터를 활용하여 진행하였다. 우리는 OE 기반 K-means알고리즘을 적용하여 데이터를 레이블링 하였고 NG누출 예측을 위한 정규화 방법 OE, MinMax, Standard, MaxAbs. Quantile 5가지 방법을 실험하였다. 그 결과 OE 기반 MLP의 인식률이 97.7%, F1-score 96.4%이며 다른 방법보다 상대적으로 높은 인식률을 보였다. 실험은 SPSS 및 Python으로 구현하였으며 실제오픈 데이터를 활용하여 실험하였다.

• 한국환경과학회지
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• 제9권5호
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• pp.397-402
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• 2000

Numerical simulation model using nesting method and considering topographic features was developed to predict atmospheric environments atmospheric flow temperature and diffusion of air pollutants in Kwangyang bay where having complex areas of point sources Korea. In addition developed simulation model was used tracing of spreading range of pollutants when a gas leaks suddenly from Yeo-cheon industrial complex. by comparing the measured and calculated data on atmospheric flow temperature and diffusion of air pollutants the results showed that this model can be well applied and complicated topography affected the diffusion of air pollutants.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.169-171
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• 2009

In recent years, environmental concerns have been raised on the use of poorly biodegradable fluids in electrical apparatus in regions where spills from leaks and equipment failure could contaminate the surroundings. For development of the environmental-friendly pole transformer using vegetable oil, we discussed the insulation construction of the transformer and the dielectric characteristics of the Nomex insulation paper in vegetable oil. Based on the experimental data, the insulation of the transformer is designed.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1531-1539
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• 2020

Searching for portable baggage through the system before boarding an airplane at an airport is important because it prevents many risks. In addition to these dangerous items, personal and confidential information leaks are occurring at airports through data storage devices. In the airport search system, there is a need for a system that searches not only dangerous items but also devices that can leak data. In this paper, we proposed a model that searches for a data storage device by improving the existing model. A comparative evaluation was conducted using existing algorithms. As a result, it was confirmed that the performance of the proposed model is 74 in the training data and 46.73 in the test data, which is superior to the existing model.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.31-38
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• 2023

In the current Internet system, there are many problems using anonymity of the network communication such as personal information leaks and crimes using the Internet system. This is why TCP/IP protocol used in Internet system does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a study for solving the above problem, there is the study of Policy Based Network Management (PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication control for every user. In this PBNM, two types of schemes exist. As one scheme, we have studied theoretically about the Destination Addressing Control System (DACS) Scheme with affinity with existing internet. By applying this DACS Scheme to Internet system management, we will realize the policy-based Internet system management. In this paper, required functions in the PBNM Scheme for multiple domains as cyber physical system that utilizes data science and AI is examined.