• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.615-629
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• 2020

Conducting Focus Group Interview (FGI), this study examined the roles and discourses of cryptocurrency's online communities and media (legacy media and YouTube), and based on this, the study proposed the direction of cryptocurrency policy. By reviewing previous literature, this study analyzed the characteristics of investors, the online community, and YouTube, which is an investment environment factor. The study figured out the purpose of use and role of the community via interviews with cryptocurrency professional investors and online community members and analyzed main discussion themes of the five top-ranked YouTube channels related to cryptocurrency with the highest number of subscribers. The results suggested that cryptocurrency's investment was led by those who are in their 20s and 30s, the investors preferred and trusted information on new media than legacy media. The online community played the role of emotional homogeneity and empathy, and YouTube mainly performed the informational role. As a result of discourse analysis and interviews, this study argued that the legal stability of cryptocurrency's policy and protection of individual investors are needed. This study's significance indicates that it used various research methods such as literature research, interviews, content analysis of community/YouTube to analyze the informational role and emotional aspects of new media and suggested policy direction of the digital new deal blockchain technology and the fairness of financial industry.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Information Systems Review
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• v.21 no.2
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• pp.117-137
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• 2019

In the Big Data era, data science has become popular with the production of numerous data in various domains, and the power of data has become a competitive power. There is a growing interest in unstructured data, which accounts for more than 80% of the world's data. Along with the everyday use of social media, most of the unstructured data is in the form of text data and plays an important role in various areas such as marketing, finance, and distribution. However, text mining using social media is difficult to access and difficult to use compared to data mining using numerical data. Thus, this study aims to develop Korean Natural Language Application (KoALA) as an integrated application for easy and handy social media text mining without relying on programming language or high-level hardware or solution. KoALA is a specialized application for social media text mining. It is an integrated application that can analyze both Korean and English. KoALA handles the entire process from data collection to preprocessing, analysis and visualization. This paper describes the process of designing, implementing, and applying KoALA applications using the design science methodology. Lastly, we will discuss practical use of KoALA through a block-chain business case. Through this paper, we hope to popularize social media text mining and utilize it for practical and academic use in various domains.

• Convergence Security Journal
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• v.22 no.2
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• pp.67-75
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• 2022

Due to the COVID-19 pandemic, many companies have introduced remote work. However, the introduction of remote work has increased attacks on companies to access sensitive information, and many companies have begun to use cloud services to respond to security threats. This study used LDA topic modeling techniques by collecting news data with the keyword 'cloud security' to analyze changes in domestic cloud security trends before and after the COVID-19 pandemic. Before the COVID-19 pandemic, interest in domestic cloud security was low, so representation or association could not be found in the extracted topics. However, it was analyzed that the introduction of cloud is necessary for high computing performance for AI, IoT, and blockchain, which are IT technologies that are currently being studied. On the other hand, looking at topics extracted after the COVID-19 pandemic, it was confirmed that interest in the cloud increased in Korea, and accordingly, interest in cloud security improved. Therefore, security measures should be established to prepare for the ever-increasing usage of cloud services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.1-7
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• 2018

The IoT environment provides an infinite variety of services using many different devices and networks. The development of the IoT environment is directly proportional to the level of security that can be provided. In some ways, lightweight cryptography is suitable for IoT environments, because it provides security, higher throughput, low power consumption and compactness. However, it has the limitation that it must form a new cryptosystem and be used within a limited resource range. Therefore, it is not the best solution for the IoT environment that requires diversification. Therefore, in order to overcome these disadvantages, this paper proposes a method suitable for the IoT environment, while using the existing block cipher algorithm, viz. the lightweight cipher algorithm, and keeping the existing system (viz. the sensing part and the server) almost unchanged. The proposed BCL architecture can perform encryption for various sensor devices in existing wire/wireless USNs (using) lightweight encryption. The proposed BCL architecture includes a pre/post-processing part in the existing block cipher algorithm, which allows various scattered devices to operate in a daisy chain network environment. This characteristic is optimal for the information security of distributed sensor systems and does not affect the neighboring network environment, even if hacking and cracking occur. Therefore, the BCL architecture proposed in the IoT environment can provide an optimal solution for the diversified IoT environment, because the existing block cryptographic algorithm, viz. the lightweight cryptographic algorithm, can be used.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.29-38
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• 2012

Due to the rapid development of network environment and wireless communication technology, the distribution of digital video has made easily and the importance of the protection for digital video has been increased. This paper proposes the digital video encryption system based on multiple chaos maps for MPEG-2 video encoding process. The proposed method generates secret hash key of having 128-bit characteristics from hash chain using Tent map as a basic block and generates $8{\times}8$ lattice cipher by applying this hash key to Logistic map and Henon map. The method can reduce the encryption overhead by doing selective XOR operations between $8{\times}8$ lattice cipher and some coefficient of low frequency in DCT block and it provides simple and randomness characteristic because it uses the architecture of combining chaos maps. Experimental results show that PSNR of the proposed method is less than or equal to 12 dB with respect to encrypted video, the time change ratio, compression ratio of the proposed method are 2%, 0.4%, respectively so that it provides good performance in visual security and can be applied in real time.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.49-57
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• 2019

The core technology development of the Fourth Industrial Revolution is linked to the development of other core technologies, which will change the industrial structure in the future and create a new smart business model. In this paper, tried to analyze the technology maturity level and analyze the technology maturity. To do this, used technology trend information to investigate and integrate the market, policy, etc. Of core technology of the 4th Industrial Revolution to achieve a comprehensive maturity level. Because technology maturity measures are scored by technology developers, prejudices may be acted upon according to a person's tendency, which may be a subjective evaluation. It is also a measure of the maturity of individual technologies, and thus is not suitable for evaluating the overall system integration perspective. However, it is possible to evaluate the maturity before integrating the core element technologies constituting the whole system and to use it as a means to compare the effect of the whole system and its feasibility and play an important role in the planning of technology development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.50-55
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• 2019

Recently Ransomware attacks are continuously increasing, and new Ransomware, which is difficult to detect just with a basic vaccine, continuously has its upward trend. Various solutions for Ransomware have been developed and applied. However, due to the disadvantages and limitations of existing solutions, damage caused by Ransomware has not been reduced. Ransomware is attacking various platforms no matter what platform it is, such as Windows, Linux, servers, IoT devices, and block chains. However, most existing solutions for Ransomware are difficult to apply to various platforms, and there is a limit that they are dependent on only some specific platforms while operating. This study analyzes the problems of existing Ransomware detection solutions and proposes the onboard module based Ransomware detection system; after the system defines the function of necessary elements through analyzing requirements that can actually reduce the damage caused by the Ransomware from the viewpoint of users, it supports various OS without pre-installation and is able to restore data even after being infected. We checked the feasibility of each function of the proposed system through the analysis of the existing technology and verified the suitability of the proposed techniques to meet the user's requirements through the questionnaire survey of a total of 264 users of personal and corporate PC users. As a result of statistical analysis of the questionnaire results, it was found that the score of intent to introduce the system was at 6.3 or more which appeared to be good, and the score of intent to change from existing solution to the proposed system was at 6.0 which appeared to be very high.

• Food Science and Industry
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• v.54 no.2
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• pp.93-101
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• 2021

Hazard Analysis Critical Control Point (HACCP) systems were developed to ensure a high level of food safety and reduced risk of foodborne illness. This paper focuses on significant issues associated with the implementation of HACCP; it provides an overview on recent literature. The structure of the paper follows six groupings of issues in the international literature of HACCP: (1) comparative studies and unification plan between HACCP and other food safety regulations; (2) verification of the HACCP system's effectiveness in improving food safety; (3) establishment of critical control point (CCP) for various foods HACCP model development; (4) expansion of HACCP application in the various fields and small businesses;(5) the impacts of HACCP on consumer's preferences and firms' financial performance in food industry; (6) HACCP and technological changes. The paper concludes with some suggestions for the future research in order to promote safe food supply chain for global customers.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.