• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.286-308
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• 2021

For a long time, dramas that everyone has enjoyed at home have become the most popular cultural contents due to the development of digital technology and the influence of Hallyu.(Korean Wave) This study was conducted in-depth interviews and participatory observations on the background, role, identity, and labor experience of TV planning producers who appeared in the drama production process with the implementation of outsourcing production policy in 1991. The number of dramas produced increased sharply in the mid-2000s due to the Korean Wave. Against this backdrop, the planning producer has expanded their scope in the drama production process and emerged as a new drama producer. The planning producer plays a role in creating an environment in which writers and directors can be selected with the identity of "not a creator but a producer of dramas" and lead drama planning. OTT and watching TV on the Internet have made it possible to watch dramas without TV. As this phenomenon accelerates and becomes commonplace, fewer consumers adhere to the traditional way of watching dramas using TV, and consumers' emotional tastes become more demanding. In this environment, TV planning producers are leading the production of dramas, exerting as much influence as writers and directors. They are also building new power relationships among drama producers by securing planning and financial power.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.387-402
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• 2020

Conventional automotive development has mainly focused on ensuring correctness and safety and security has been relatively neglected. However, as the number of automotive hacking cases has increased due to the increased Internet connectivity of automobiles, international organizations such as the United Nations Economic Commission for Europe(UNECE) are preparing cybersecurity regulations to ensure security for automotive development. As with other IT products, automotive cybersecurity regulation also emphasize the concept of "Security by Design", which considers security from the beginning of development. In particular, since automotive development has a long lifecycle and complex supply chain, it is very difficult to change the architecture after development, and thus Security by Design is much more important than existing IT products. The problem, however, is that no specific methodology for Security by Design has been proposed on automotive development process. This paper, therefore, proposes a specific methodology for Security by Design on Automotive development. Through this methodology, automotive manufacturers can simultaneously consider aspects of functional safety, and security in automotive development process, and will also be able to respond to the upcoming certification of UNECE automotive cybersecurity regulations.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.245-251
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• 2021

As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.125-137
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• 2007

This paper proposes a new scheme to provide the location privacy of sources in Wireless Sensor Networks (WSNs). Because the geographical location of a source sensor reveals contextual information on an 'event' in WSN, anonymizing the source location is an important issue. Despite abundant research efforts, however, about data confidentiality and authentication in WSN, privacy issues have not been researched well so far. Moreover, many schemes providing the anonymity of communication parties in Internet and Ad-hoc networks are not appropriate for WSN environments where sensors are very resource limited and messages are forwarded in a hop-by-hop manner through wireless channel. In this paper, we first categorize the type of eavesdroppers for WSN as Global Eavesdropper and Compromising Eavesdropper. Then we propose a novel scheme which provides the anonymity of a source according to the types of eavesdroppers. Furthermore, we analyze the degree of anonymity of WSN using the entropy-based modeling method. As a result, we show that the proposed scheme improves the degree of anonymity compared to a method without any provision of anonymity and also show that the transmission range plays a key role to hide the location of source sensors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.5
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• pp.93-106
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• 2006

With the rapid growth of the wireless Internet communication, a new generation of mobile devices have made possible the broad distribution of mobile digital contents, such as image, music, video, games and applications over the wireless Internet. Mobile devices are rapidly becoming the major means to extend communication channels without copy Protection, usage rule controlling and authentication. As a result, mobile digital contents may be illegally altered, copied and distributed among unauthorized mobile devices. In this paper, we take a look at Open Mobile Alliance (OMA) DRM v2.0 in general, its purpose and function. The OMA is uniquely the focal point for development of an open standard for mobile DRM. Next we introduces features for an active content and illustrates the difference between an active content and an inactive content. Enabling fast rendering of an active content, we propose an OMA-based DRM framework. This framework include the following: 1) Extending DCF Header for supporting an selective encryption, 2) Content encryption key management, 3) Rendering API for an active content. Experimental results show that the proposed framework is able to render an active content fast enough to satisfy Quality of Experience. %is framework has been proposed for a mobile device environment, but it is also applicable to other devices, such as portable media players, set-top boxes, or personal computer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.104-112
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• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.279-284
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• 2020

In January 2020, COVID19 plunged the whole planet into a pandemic. This has caused great economic losses and is causing social confusion. COVID19 has a superior infection rate among people with underlying disease such as heart disease, high blood pressure, diabetes, stroke, depression, and cancer. In addition, it was studied that patients with underlying disease had a higher fatality rate than those without underlying disease. In this study, the presence or absence of underlying disease was classified using EEG data. The data used to classify the presence or absence of underlying disease was EEG data provided by Data Science lab, consisting of 33 features and 69 samples. Z-score was used for data pretreatment. Classification was performed using the neural network NEWFM and ZNN engine. As a result of the classification of the presence or absence of the underlying disease, the experimental results were 77.945 for NEWFM and 76.4% for ZNN. Through this study, it is expected that EEG data can be measured, the presence or absence of an underlying disease is classified, and those with a high infection rate can be prevented from COVID19. Based on this, there is a need for research that can subdivide underlying disease in the future and research on the effects of each underlying disease on infectious disease.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.