• Journal of the Society of Cosmetic Scientists of Korea
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• v.30 no.4 s.48
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• pp.533-541
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• 2004

Atopic dermatitis, also called congenital fever, is a allergic eczema of chronic itching disease. It is a recurrent and familial disease and appears on a wide age group from infant to adult. It is very common, and the ratio of occurrence is about $9{\~}l2\%$ of a child. However. it is showing trend of continuous increase by social and natural environment, food culture, and life style, recently. The human skin plays a barrier role against a physical and chemical stimulus from external environment. According to the latest study, the decreased amount of ceramide in horny layer impairs the bier function and moisture-maintaining function of skin in atopic dematitis patient. Ceramide is a kind of the sphingolipid in which a fatty acid is connected to sphingosin. Ceramide constitutes about $40\%$ of total lipid between keratinocytes and has the function of defense wall and building regular structure to suppress moisture vaporization in horny layer. In horny layer of skin a comified cell is composed of multi-layer structure of a brick shape, and, as for this cornified cell, it is strongly connected by ceramide, cholesterol, and free fatty acid. Here, we described the effects of a cream containing ceramide on the recovery of skin harrier function of atopic dermatitis patient. The safety and efficacy of latex and liquid formula were evaluated as cosmetics for atopic dermatitis. The latex products was composed of intercellular lipid components-ceramide, cholesterol, and free fatty acid-to restore skin barrier function in atopic dermatitis patients. The liquid one contained beta-glucan, magnolia extracts, and licolice extracts, which have skin immunomodulatory and anti-inflammatory effects. It is also confirmed that their possibility on new cosmetic market of atopic dermatitis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.94-103
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• 2017

As automatic hacking tools and techniques have been improved, the number of new vulnerabilities has increased. The CVE registered from 2010 to 2015 numbered about 80,000, and it is expected that more vulnerabilities will be reported. In most cases, patching a vulnerability depends on the developers' capability, and most patching techniques are based on manual analysis, which requires nine months, on average. The techniques are composed of finding the vulnerability, conducting the analysis based on the source code, and writing new code for the patch. Zero-day is critical because the time gap between the first discovery and taking action is too long, as mentioned. To solve the problem, techniques for automatically detecting and analyzing software (SW) vulnerabilities have been proposed recently. Cyber Grand Challenge (CGC) held in 2016 was the first competition to create automatic defensive systems capable of reasoning over flaws in binary and formulating patches without experts' direct analysis. Darktrace and Cylance are similar projects for managing SW automatically with artificial intelligence and machine learning. Though many foreign commercial institutions and academies run their projects for automatic binary analysis, the domestic level of technology is much lower. This paper is to study developing automatic detection of SW vulnerabilities and defenses against them. We analyzed and compared relative works and tools as additional elements, and optimal techniques for automatic analysis are suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.146-152
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• 2016

The purposes of this study were to investigate the effects of professional soldiers' family healthiness on the organizational effectiveness of the military and test for any differences in the structural modeling path caused by the mediating effects of empowerment depending on whether they lived with their families or not. Data were collected with a self-administered survey distributed to 480 professional soldiers after the approval of the Ministry of National Defense. The data collected were then analyzed using descriptive statistics, structural equation modeling, and multi-group analysis. The analysis results show that the family healthiness of all of the professional soldiers had direct or indirect effects on their organizational effectiveness via empowerment and that family healthiness was a variable with a very large explanatory power for organizational effectiveness. Both family healthiness and empowerment had effects on the organizational effectiveness of the solders that were living with their families and those who lived separately from their families. The levels of family healthiness, empowerment, and organizational effectiveness were all high among the professional soldiers that were living with their families and all low among those who were not. Increasing the family healthiness and empowerment was found to enhance the organizational effectiveness in all of the models, and a higher organizational effectiveness would be expected from professional soldiers living with their families. These findings led to a proposal that various plans should be prepared to help professional soldiers live with their families and provide them with family welfare support.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.27-36
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• 2015

Current leakage of industrial technologies with revealing state secret against nation is gradually increasing and scope of the spill is diversified from technology-oriented leakage to new economic security sector like information and communication, electrical and electronic, defense industry, illegal export of strategic material, economic order disturbance by foreign country, infringement of intellectual property, etc. So the spill damage can affect not only leaked company but also national interests and entire domestic industry. According to statistics from National Industrial Security Center of National Intelligence Service, a major cause of technology leakage is not only by external things about hacking and malignant code, but internal leakage of former and current employees account for about 80%. And technology leakage due to temptation of money and personal interests followed by technology leakage of subcontractor is steadily increased. Most studies in the field of security have tended to focus on measuring security capability of company in order to prevent leakage core assets or developing measurement Indicators for management rather than security activities of the company members that is most important. Therefore, this study analyzes the effect of most underlying security education in security activities on security capabilities of enterprise. As a result, it indicates that security education have a positive(+) correlation with security capabilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.7
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• pp.479-490
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• 2014

A lower extremity exoskeleton is a robot device that attaches to the lower limbs of the human body to augment or assist with the walking ability of the wearer. In order to improve the wearer's walking ability, the robot senses the wearer's walking locomotion and classifies it into a gait-phase state, after which it drives the appropriate robot motions for each state using its actuators. This paper presents a method by which the robot senses the wearer's locomotion along with a novel classification algorithm which classifies the sensed data as a gait-phase state. The robot determines its control mode using this gait-phase information. If erroneous information is delivered, the robot will fail to improve the walking ability or will bring some discomfort to the wearer. Therefore, it is necessary for the algorithm constantly to classify the correct gait-phase information. However, our device for sensing a human's locomotion has very sensitive characteristics sufficient for it to detect small movements. With only simple logic like a threshold-based classification, it is difficult to deliver the correct information continually. In order to overcome this and provide correct information in a timely manner, a probabilistic gait-phase classification algorithm is proposed. Experimental results demonstrate that the proposed algorithm offers excellent accuracy.

• Korean Security Journal
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• no.58
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• pp.215-241
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• 2019

This study approached the military security problem from the viewpoint of social psychology in view of the fact that the military security problem has been focused on the technical field such as cyber security along with the development of defense science and technology. In this background, we examined the causal relationship between variables after extracting variables affecting the security problem of the military through previous research. The significance of the study results is as follows. First, the military culture has a direct and indirect influence on improving the willingness to adhere to security and the security level of military organizations, as well as contributing to the establishment of military values. This indicates that the overall organizational culture of the current military is influencing the security consciousness of the soldiers and the achievement of organizational security, while at the same time showing the need for effort to create the right organizational culture. Second, the values of soldiers had a positive effect on the willingness of the individual to obey the security and the security performance. Values begin with an understanding of organizational culture and indicate that efforts can be made to establish an organizational security posture when the right values are formed. Third, we have improved the completeness of the study by verifying the causal relationship by extracting variables that correspond to the context of the ROK military.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.