This research establishes a security evaluation model from the insider leakage perspective and suggests an objective evaluation measurement. Organizational security risks are fused and compounded both inside and outside the organization. Although multiple security controls are implemented to minimize an organization’s security risk, effective security control requires management to preemptively check the organization’s security level. Existing criteria for evaluating security level are limited to external security risks and have improper limit points for dealing with security risks that are fused and compounded within an organization. The focus of this study is the prevention of technical information leakage. Furthermore, we propose a method for measuring the level at which the objectivity of certain items is secured. We compiled 26 detailed evaluation items, considering the security requirements to prevent technical information leakage. We not only performed suitability, reliability, and factor analyses and statistical validation, but also established a method to measure the security level. This measurement method ensures the effectiveness and objectivity of the evaluation of security level, mitigating the risks of security incidents caused by insiders. The results serve as a reference for organizations when designing security evaluation criteria and automated tools based on our evaluation model for future research.

This paper implements an estimated closeness centrality ranking algorithm in large-scale workflow-supported social networks and performance analyzes of the algorithm. Existing algorithm has a time complexity problem which is increasing performance time by network size. This problem also causes ranking process in large-scale workflow-supported social networks. To solve such problems, this paper conducts comparison analysis on the existing algorithm and estimated results by applying estimated-driven RankCCWSSN(Rank Closeness Centrality Workflow-supported Social Network). The RankCCWSSN algorithm proved its time-efficiency in a procedure about 50% decrease.

The medical convergence industry has gradually adopted ICT devices, which has led to legacy security problems related to ICT devices. However, it has been difficult to solve these problems due to data resource issues. Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information. Therefore, to provide reliable services focused on security internalization, it is necessary to establish a medical convergence environment-oriented security management system. This study proposes the use of system identification and countermeasures to secure system reliability when using medical convergence environment information in medical artificial intelligence. We checked the life cycle of medical information and the flow and location of information, analyzed the security threats that may arise during the life cycle, and proposed technical countermeasures to overcome such threats. We verified the proposed countermeasures through a survey of experts. Security requirements were defined based on the information life cycle in the medical convergence environment. We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

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<p>The mass extinction efficiency(MEE), which indicates the degree of aerosol extinction(scatter and absorption) per unit PM mass concentration, is an important factor in converting optical concentration into mass concentration. Because its value varies depending on the particles' size and composition, which are particles' characteristics. In this study, the extinction coefficients of coarse and fine particles were calculated using the LiDAR data of Seoul observed by NIES(Japan's National Institute of Environmental Studies) and the visibility data of Seoul observed by the Korea Meteorological Administration. In the case of lidar data, two wavelengths (532nm, 1064nm) measured by lidar were used to calculate extinction coefficients, and the wavelength of 532 nm (532P and 532S) were used to classify extinction coefficients into coarse particles(PM10-2.5) and fine particles(PM2.5). In the case of visibility data, the PM10 and PM2.5 extinction coefficients were calculated using the equation of Koschmieder (1924) and Cheng et al. (2017). The PM10, PM10-2.5, and PM2.5 respective MEE were calculated using Seoul data of PM10 and PM2.5 at the same time provided by the Korea Environment Corporation. The relative humidity data provided by the Korea Meteorological Administration were divided into seven sections less than 40%, 40~49%, 50~59%, 60~69%, 70~79%, 80~89%, and 90~100%. According to relative humidity, this study examined the change of the calculated MEE. This study analyzes the effect of relative humidity on the Hygroscopic Growth of PM10, PM10-2.5, and PM2.5.</p><div> </div>