• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• Research in Plant Disease
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• v.27 no.3
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• pp.120-127
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• 2021

Plant viruses cause significant yield losses, continuously compromising crop production and thus representing a serious threat to global food security. Tomato spotted wilt virus (TSWV) is the most harmful plant virus that mainly infects horticultural crops and has a wide host range. Reverse-transcription quantitative real-time PCR (RT-qPCR) has been widely used for detecting TSWV with high sensitivity, but its application is limited owing to the lack of standardization. Therefore, in this study, a sensitive and accurate reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) method was established for TSWV detection. Additionally, we compared the sensitivities of RT-qPCR and RT-ddPCR for TSWV detection. Specificity analysis of RT-ddPCR for TSWV showed no amplification for main pepper viruses and negative control. TSWV transcripts levels measured by RT-ddPCR and RT-qPCR showed a high degree of linearity; however, the former yielded results that were at least 10-fold more sensitive and detected lower TSWV copy numbers than the latter. Collectively, our findings show that RT-ddPCR provides improved analytical sensitivity and specificity for TSWV detection, making it suitable for identifying low TSWV concentrations in field samples.

• 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 Digital Forensics
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• v.12 no.3
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• pp.109-120
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• 2018

The smartphone is the communication device that is the most personal to the user, and it keeps a lot of information related to the user and makes information communication with other devices. With these characteristics, forensics on smartphones are one of the most basic methods of investigation in criminal investigations, and have actually contributed to the settlement of the case by providing many clues. However, recently, it is designed to encrypt data stored as a social issue related to the protection of user's personal information, or to delete deleted data or to delete log data together. So, any solutions? In this paper, I try to find the answer from cloud data stored by smartphone user account. Cloud forensics should approach complementary relationships rather than smartphone forensics. There are a lot of data stored in the cloud that can be meaningfully used in the investigation. Online activity information of users, such as Internet usage, YouTube view, and contents purchase information, cloud service such as e-mail, cloud drive, and location information are the most representative data. These data can be unvaluable, but here are some important clues in various types of criminal investigations. In this paper, I propose a method to extract data from the google cloud so that the data can be used for investigation, and to utilize the extracted data for investigation. And it explains the role of the extracted artifacts in the actual investigation business through virtual cases and proves its value.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.660-670
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• 2019

The purpose of this study is to elaborate the logical configuration of livelihood benefits for the institutionalized recipients under the renewed custom-tailored benefit system of National Basic Livelihood Security System(NBLSS) and to present appropriate level of benefits in terms of coherency of the system. In July 2015, the NBLSS was reformed to adopt a relative level of benefit standard for the general recipients according to certain amount of ratio of standard median income. However, the benefit for the institutionalized recipients was still based on the cost of necessities of absolute poverty level. It is at this juncture that this study suggests livelihood benefits for the institutionalized recipients reflect standard median income to comply with the reform of the NBLSS. To this end, this study firstly derives basic living items for the institutionalized recipients based on the literature review and FGI. Secondly, it calculates the reflection ratio of livelihood benefits utilizing Household Trend Survey's consumption data under 40%. Finally, it applies equivalence scale of households to adjust the under-represented scale for large size institutions. To continue the reflection ratio method, it is necessary to review the consumption trends and the stability of the reflection ratio periodically.

• Korean Journal of Labor Studies
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• v.24 no.2
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• pp.247-291
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• 2018

This study investigates the employment structure and the social safety net experience of the subcontracting cleaning workers in Korea, who have been main targets of the labor outsourcing despite the necessity and permanence of their labour. This study specifically focuses on the fact that these subcontracting cleaning workers are mostly female and in their old age, and analyzes how the combination of their age, gender, and employment structure leads to the (mis)match with the Korean social security system. Case study with in-dept interview method has been conducted to the old-aged female subcontracting cleaning workers in Korea. The result of this study is as follows. It was the income insecurity that led them to (re)enter the labour market, and the cleaning work was the almost the only wage work they could do considering their age and gender. Cleaning workers are mostly employed in the subcontracting company, and thus their labour contracts depend on the business contract period between the original and subcontracting company. Consequently, their employment relationship is mostly insecure unless they are guaranteed employment succession through the collective agreement of trade union. Moreover, it has been discovered that the employment insecurity due to the indirect employment relationship led to the poor labour conditions, low wage, and the exclusion from the social safety net.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.1-11
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• 2019

Purpose: This study intends to determine how disaster prevention characteristics of important state-designated wooden cultural properties in Gyeongsangnam-do vary according to the surrounding environments and to examine disaster prevention measures for wooden cultural properties that fit their surrounding environments accordingly. Method: The designation status and characteristics of cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do were identified, and the damage status of cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do was reviewed based on the history of disasters. Also, the disaster prevention environments for 58 state-designated wooden cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do were analyzed separately into mountainous area, rural area and urban area, topographic characteristics were drawn. Results: For cultural properties located in urban areas, it was found that security guards were arranged properly and disaster prevention training was carried out well. In addition, access condition to the cultural properties was adequate; prompt access to such properties was possible. In rural areas, flame retardant works have been undertaken properly and many cultural properties were found to be located on a flat ground. Mountainous areas had highly inadequate access condition to cultural properties and disasters occurred most frequently in these areas in the past. Conclution: First, for wooden cultural properties located in urban areas, it is necessary to secure the self-defense fire service manpower for an initial response and reinforce the disaster prevention education. Second, for wooden cultural properties located in rural areas, prevention projects such as insect control project and disaster prevention insurance should be carried out in order to protect the cultural properties. Third, as for wooden cultural properties located in mountainous areas, it is necessary to prepare establish to reinforce self-response capability.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.89-96
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• 2021

In this paper, we analyzed that the user authentication scheme for TMIS(Telecare Medicine Information System) proposed by Al-Saggaf et al. In 2019, Al-Saggaf et al. proposed authentication scheme using biometric information, Al-Saggaf et al. claimed that their authentication scheme provides high security against various attacks along with very low computational cost. However in this paper after analyzing Al-Saggaf et al's authentication scheme, the Al-Saggaf et al's one are missing random number s from the DB to calculate the identity of the user from the server, and there is a design error in the authentication scheme due to the lack of delivery method. Al-Saggaf et al also claimed that their authentication scheme were safe against a variety of attacks, but were vulnerable to password guessing attack using login request messages and smart cards, session key exposure and insider attack. An attacker could also use a password to decrypt the stored user's biometric information by encrypting the DB with a password. Exposure of biometric information is a very serious breach of the user's privacy, which could allow an attacker to succeed in the user impersonation. Furthermore, Al-Saggaf et al's authentication schemes are vulnerable to identity guessing attack, which, unlike what they claimed, do not provide significant user anonymity in TMIS.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.177-188
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• 2019

Recently, citizens are feeling anxious as 'Motiveless Crime' increases. The quality of citizens life is degraded and the degree of crime fear is increasing. In this study, based on various variables related to crime other than actual crime occurrence status, crime occurrence points (point line polygon) felt by citizens are created by using mental map methodology. And the purpose of this study is to derive the area of concern for crime through spatial overlap analysis using kernel density estimation analysis. It also uses spatial overlay analysis using kernel density estimation to derive areas of concern for crime occurrence. As a result, the local residents' request point and the areas of concern for crime were overlapped. In addition, the mental map indicating the fear of crime was constructed by mapping mainly the areas between the facilities, the non-construction area such as the narrow area, the security CCTV, the streetlight. This study is meaningful in that it tried to derive a crime occurrence concern area by using mental map method unlike the previous study related to crime. The results of this study, such as mental map, could be used in various fields such as construction of fragile crime map, guideline of crime prevention through environment design.