• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.76-86
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• 2016

SQL Injection attacks are the most widely used and also they are considered one of the oldest traditional hacking techniques. SQL Injection attacks are getting quite complicated and they perform a high portion among web hacking. The big data environments in the future will be widely used resulting in many devices and sensors will be connected to the internet and the amount of data that flows among devices will be highly increased. The scale of damage caused by SQL Injection attacks would be even greater in the future. Besides, creating security solutions against SQL Injection attacks are high costs and time-consuming. In order to prevent SQL Injection attacks, we have to operate quickly and accurately according to this data analysis techniques. We utilized data analytics and machine learning techniques to defend against SQL Injection attacks and analyzed the execution plan of the SQL command input if there are abnormal patterns through checking the web log files. Herein, we propose a way to distinguish between normal and abnormal SQL commands. We have analyzed the value entered by the user in real time using the automated SQL Injection attacks tools. We have proved that it is possible to ensure an effective defense through analyzing the execution plan of the SQL command.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.140-145
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• 2019

With the development of IT technology, many changes are taking place in the health service environment over the past. However, even if medical technology is converged with IT technology, the problem of medical costs and management of health services are still one of the things that needs to be addressed. In this paper, we propose a model for hospitals that have established the IoT system to efficiently analyze and manage the personal information of users who receive medical services. The proposed model aims to efficiently check and manage users' medical information through an in-house IoT system. The proposed model can be used in a variety of heterogeneous cloud environments, and users' medical information can be managed efficiently and quickly without additional human and physical resources. In particular, because users' medical information collected in the proposed model is stored on servers through the IoT gateway, medical staff can analyze users' medical information accurately regardless of time and place. As a result of performance evaluation, the proposed model achieved 19.6% improvement in the efficiency of health care services for occupational health care staff over traditional medical system models that did not use the IoT system, and 22.1% improvement in post-health care for users who received medical services. In addition, the burden on medical staff was 17.6 percent lower on average than the existing medical system models.

• Journal of Convergence for Information Technology
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• v.9 no.4
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• pp.1-7
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• 2019

The medical environment, combined with IT technology, is changing the paradigm for medical services from treatment to prevention. In particular, as ICT convergence digital healthcare technology is applied to hospital medical systems, infrastructure technologies such as big data, Internet of Things, and artificial intelligence are being used in conjunction with the cloud. In particular, as medical services are used with IT devices, the quality of medical services is increasingly improving to make them easier for users to access. Medical institutions seeking to incorporate IoT services into cloud health care environment services are trying to reduce hospital operating costs and improve service quality, but have not yet been fully supported. In this paper, a patient information collection model from hospital IoT system, which has established a cloud environment, is proposed. The proposed model prevents third parties from illegally eavesdropping and interfering with patients' biometric information through IoT devices attached to the patient's body at hospitals in cloud environments that have established hospital IoT systems. The proposed model allows clinicians to analyze patients' disease information so that they can collect and treat diseases associated with their eating habits through IoT devices. The analyzed disease information minimizes hospital work to facilitate the handling of prescriptions and care according to the patient's degree of illness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.85-91
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• 2020

Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.

• Journal of Korean Library and Information Science Society
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• v.54 no.3
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• pp.57-80
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• 2023

The purpose of this study is to examine the current status of university library operation through the five laws of library science and suggest ways to improve it. To this end, the operation status of four national and public university libraries was analyzed with the scope of the study as the Busan area. Furthermore, for an in-depth investigation, the P University library in Busan was examined as a case. The operation status of national and public university libraries in the Busan region was analyzed by dividing them into the categories of university library collection materials, facilities, staff, material purchase costs, and users from 2018 to 2022. In particular, there was a decrease in users and a shortage of librarians due to COVID-19. In the case study, the operation status of P University library was investigated focusing on the first and fifth laws of Ranganathan's five laws of library science. In the first law, the study was conducted on the aspects of access to and use of the collection, library location, usage time and book lending, and staff. In the fifth law, the investigation was divided into service improvement aspects according to changes in digital technology of P University library and library environment improvement aspects according to changes in user characteristics. As a result of the study, 'establishment of library space composition strategy', 'establishment of blue ocean strategy', and 'expansion of recruitment and deployment of librarians' were suggested as improvement plans.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.4
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• pp.355-363
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• 2023

In safety assessment of a aged bridge, dynamic characteristics and displacement are directly related to the rigidity of the structural system, especially displacement is the most important factor as the physical quantity that the bridge user can directly detect. However, in order to measure the displacement of the bridge, it is difficult to install displacement sensors at the bottom of the bridge and conduct traffic blocking and loading tests, resulting in increased costs or impossible measurements depending on the bridge's environment. In this study, a method of measuring the displacement of a bridge using only accelerometers without installing displacement sensors and ambient vibration without a loading test was proposed. For the analysis of bridge dynamic characteristics and displacement using ambient vibration, the mode shape and natural frequency of the bridge were extracted using a TDD technique known to enable quick analysis with simple calculations, and the unit load displacement of the bridge was analyzed through flexibility analysis to calculate static displacement. To verify this proposed technology, an on-site test was conducted on C Bridge, and the results were compared with the measured values of the loading test and the structural analysis data. As a result, it was confirmed that the mode shape and natural frequency were 0.42 to 1.13 % error ratio, and the maximum displacement at the main span was 3.58 % error ratio. Therefore, the proposed technology can be used as a basis data for indirectly determine the safety of the bridge by comparing the amount of displacement compared to the design and analysis values by estimating the displacement of the bridge that could not be measured due to the difficulty of installing displacement sensors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.207-212
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• 2024

The importance of hydrogen fuel cells continues to be emphasized, and there is a growing demand for education and training in this field. Among various educational environments, metaverse education is opening a new era of change in the global education industry, especially to adapt to remote learning. The most significant change that the metaverse has brought to education is the shift from one-way, instructor-centered, and static teaching approaches to multi-directional and dynamic ones. It is expected that the metaverse can be effectively utilized in hydrogen fuel cell engineer education, not only enhancing the effectiveness of education by enabling learning and training anytime, anywhere but also reducing costs associated with engineering education.In this research, inspired by these ideas, we are designing a fuel cell education platform. We have created a platform that combines theoretical and practical training using the metaverse. Key aspects of this research include the development of educational training content to increase learner engagement, the configuration of user interfaces for improved usability, the creation of environments for interacting with objects in the virtual world, and support for convergence services in the form of digital twins.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.763-780
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• 2024

As the domestic and international landscape undergoes rapid changes, the importance of implementing security measures in response to the growing threats that businesses face is increasing. In this context, the need for Security by Design (SbD), integrating security from the early design stages, is becoming more pronounced, with threat modeling recognized as a fundamental tool of SbD. Particularly, to save costs and time by detecting and resolving security issues early, the application of the Shift Left strategy requires the involvement of personnel with limited security expertise, such as software developers, in threat modeling. Although various automated threat modeling tools have been released, their lack of user-friendliness for personnel lacking security expertise poses challenges in conducting threat modeling effectively. To address this, we conducted an analysis of research related to threat modeling tools and derived usability evaluation criteria based on the GQM(Goal-Question-Metric) approach. An expert survey was conducted to validate both the validity and objectivity of the derived criteria. We performed usability evaluations of three threat modeling tools (MS TMT, SPARTA, PyTM), and the evaluation results led to the conclusion that MS TMT exhibited superior usability compared to other tools. This study aims to contribute to the creation of an environment where personnel with limited security expertise can effectively conduct threat modeling by proposing usability evaluation criteria.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.