• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.47-58
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• 2015

Recently, new information security vulnerabilities have proliferated with the convergence of information security environments and information and communication technology. Accordingly, new types of cybercrime are on the rise, and security breaches and other security-related incidents are increasing rapidly because of security problems like external cyberattacks, leakage by insiders, etc. These threats will continue to multiply as industry and technology converge. Thus, the main purpose of this paper is to design and present security subjects in order to train professional security management talent who can deal with the enhanced threat to information. To achieve this, the study first set key information security topics for business settings on the basis of an analysis of preceding studies and the results of a meeting of an expert committee. The information security curriculum taxonomy is developed with reference to an information security job taxonomy for domestic conditions in South Korea. The results of this study are expected to help train skilled security talent who can address new security threats in the future environment of industrial convergence.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.1
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• pp.87-97
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• 2005

A secure index search protocol let us search the index of encrypted documents using the trapdoor for a keyword. It enables an untrusted server to learn nothing more than the search result about the documents without revealing the keyword. A lot of secure search protocols have been suggested but they only considered the search between a single-user and a server. In real organizations such as government offices or enterprises where exist many hierarchical departments, the search system for groups is arisen more often. In this paper, we construct secure index search protocols for hierarchical group settings without re-encryption of the old encrypted documents when group keys are re-keyed newly.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.79-90
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• 2017

If digital forensic investigators can utilize file access logs when they audit insider information leakage cases or incident cases, it would be helpful to understand user's behaviors more clearly. There are many known artifacts related to file access in MS Windows. But each of the artifacts often lacks critical information, and they are usually not preserved for enough time. So it is hard to track down what has happened in a real case. In this thesis, I suggest a method to utilize SACL(System Access Control List) which is one of the audit functions provided by MS Windows. By applying this method of strengthening the Windows's audit settings, even small organizations that cannot adopt security solutions can build better environment for conducting digital forensic when an incident occurs.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.347-356
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• 2019

Recently, the liberalization of industrial control systems has been accompanied by a major change in the structural paradigm of information systems in the public and public sectors, and potential cyber security problems in the future major infrastructure control systems that cannot respond to the level of security of existing information systems. To cope with this, a cyber security evaluation tool that can evaluate security vulnerability in three dimensions against various infrastructure control system environment is needed. However, a cyber security evaluation in the domestic environments does not have the concept of the current security status and satisfy settings of the infrastructure. Also, the most of items in that environments have had short-term inspection themselves which makes a limitation by a technical area. In order to overcome this problems, many researches are needed to apply CSET (Cyber Security Evaluation Tool) which is the US cyber security evaluation tool to the control environment of various domestic infrastructure. In this paper, first, we analyze methods to apply to the major domain through the analysis of various case studies on existing security assessement tools. Finally, we discuss future directions.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Korean Home Economics Association
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• v.35 no.6
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• pp.317-334
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• 1997

The purpose of this Study is to theoretically discuss the Family Business. Family Business means a business that is owned and managed by one or more family members. Family Businesses are seen as an avenue to achieve economic security, as motivations for a productive society, and as a method for individuals to receive a monetary return for their talents and initiative. The presence of family business as a predominant business structure in the all of country's economy and their association economic contributions have been documented elsewhere. The overall objectives of this study are to introduce in the Home economics and Home Management field what the Family Business is, concretely to identify the definition of Family Business and related concepts-Home Based Business & Home Based work-, to study the interface of family and business functions within a single family. This study is to examine a clear understanding of the relationship between family functioning and business viability in families who own and operate businesses. Such understanding will be helpful in enhancing the stability and security of families who own and operate business and in developing policies and programs that foster Family Business and assist in their contributions to community and economic development. Also to understand the family and business environments and their interaction can enhance the opportunities and satisfactions for family members who are involved in business together. Results from this study will allow researchers a unique view of the Family business management and will contribute to individual and group well-being in both family and work settings.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.35-41
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• 2016

Internet of Things (IoT) services are widely used in appliances of daily life and industries. IoT services also provide various conveniences to users and are expected to affect value added of all industries and national competitiveness. However, a variety of security threats are increased in IoT environments and lowers reliability of IoT devices and services that make some obstacles for commercialization. The attacks arising in IoT environments are making industrial and normal life accidents unlike existing information leak and monetary damages, and can expand damage scale of leakage of personal information and privacy more than existing them. To solve these problems, we design a session key based access control scheme for secure communications in IoT environments. The proposed scheme reinforces message security by generating session key between device and access control network system. We analyzed the stability of the proposed access scheme in terms of data forgery and corruption, unauthorized access, information disclosure, privacy violations, and denial of service attacks. And we also evaluated the proposed scheme in terms of permission settings, privacy indemnity, data confidentiality and integrity, authentication, and access control.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.19-30
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• 2021

In this paper, when SIDH is instantiated using only 3- and 5-isogeny, we demonstrate which curve is more efficient among the Montgomery, Edwards, and Huff curves. To this end, we present the computational cost of the building blocks of SIDH on Montgomery, Edwards, and Huff curves. We also present the prime we used and parameter settings for implementation. The result of our work shows that the performance of SIDH on Montgomery and Huff curves is almost the same and they are 0.8% faster than Edwards curves. With the possibility of using isogeny of degree other than 3 and 4, the performance of 5-isogeny became even more essential. In this regard, this paper can provide guidelines on the selection of the form of elliptic curves for implementation.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.269-275
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• 2022

Public healthcare has recently become an issue of great importance due to the exponential growth in the human population, the increase in medical expenses, and the COVID-19 pandemic. Speed is one of the crucial factors in saving life, particularly in case of heart attack. Therefore, a healthcare device is needed to continuously monitor and follow up heart health conditions remotely without the need for the patient to attend a medical center. Therefore, this paper proposes a portable electrocardiogram (ECG) monitoring system to improve healthcare for heart attack patients in both home and ambulance settings. The proposed system receives the ECG signals of the patient and sends the ECG values to a MySQL database on the IoT-cloud via Wi-Fi. The signals are displayed as an ECG data chart on a webpage that can be accessed by the patient's doctor based on the HTTP protocol that is employed in the IoT-cloud. The proposed system detects the ECG data of the patient to calculate the total number of heartbeats, number of normal heartbeats, and the number of abnormal heartbeats, which can help the doctor to evaluate the health status of the patient and decide on a suitable medical intervention. This system therefore has the potential to save time and life, but also cost. This paper highlights the five main advantages of the proposed ECG monitoring system and makes some recommendations to develop the system further.