• KIPS Transactions on Computer and Communication Systems
• /
• v.5 no.12
• /
• pp.463-470
• /
• 2016

With the growing demand for MAC-based system, the need for digital forensic techniques of these system has been increasing. In the digital forensic analysis process, sometimes analysts have recovered the deleted files when they prove the allegations if system user try to remove the evidence deliberately. Research and analysis that recover the deleted files from a file system constantly been made and HFS+ that is a file system of MAC-based system also has been researched. Carving techniques primarily has been used to recover the deleted file from HFS+ a file system because metadata of folder or file overwrite metadata of a deleted file when file is deleted from a file system on HFS+ characteristic. But if the file content is saved by separated state in a file system, Carving techniques also can't recover the whole or a part of the deleted file. In this paper we describe technique the deleted file recovery technique using HFS+ file system a journal. This technique that is suggested by existing research and analysis result is the technique that recover the deleted file by metadata that is maintained in a journal on HFS+ file system. but this technique excludes specific files and this problem needs to be reformed. In this paper we suggest algorithm that analysis a journal of HFS+ file system in detail. And we demonstrate that the deleted file cat be recovered from the extracted metadata by this algorithm without the excluded file.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.9
• /
• pp.440-446
• /
• 2016

Military secrets or confidential data of any organization are extremely important assets. They must be discluded from outside. To do this, methods for detecting anomalous attacks and intrusions inside the network have been proposed. However, most anomaly-detection methods only cover aspects of intrusion from outside and do not deal with internal leakage of data, inflicting greater damage than intrusions and attacks from outside. In addition, applying conventional anomaly-detection methods to data exfiltration creates many problems, because the methods do not consider a number of variables or the internal network environment. In this paper, we describe issues considered in data exfiltration detection for anomaly detection (DEDfAD) to improve the accuracy of the methods, classify the methods as profile-based detection or machine learning-based detection, and analyze their advantages and disadvantages. We also suggest future research challenges through comparative analysis of the issues with classification of the detection methods.

• Knowledge Management Research
• /
• v.22 no.3
• /
• pp.235-252
• /
• 2021

The development of information and communication technology and COVID-19 have caused an unusual change in the hotel industry, and the demand for the contactless services such as service robots from hotel customers has surged. Therefore, this study investigates the perception of hotel customers on contactless services by applying a mixed-method analysis. Specifically, this study identified the causal correlations between variables through the structural equation model, and further applied the fuzzy set qualitative comparison analysis to derive patterns of variables that form the intention to use the non-face-to-face services. As a result of the analysis, it was shown that service experience co-creation, palyfulness, personalization, and trust had a significant effect on intention to use through the contactless service use desire. On the other hand, in the results of fuzzy-set qualitative comparison analysis, playfulness was derived as a core factor in all patterns. Based on these analysis results, this study provides academic basis for in-depth understanding of hotel customers' perception of contactless service and specific guidelines for hotel managers on the contactless service strategies in the era of COVID-19 pandemic.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.353-359
• /
• 2021

The Army is constructing a training system using Miles equipment that applies the latest science and technology to carry out military training. The Miles training system is a system that uses Miles equipment to simulate the damage situation of combat personnel and equipment in the same way as an actual battlefield by conducting practiced maneuvers in the field. Through this, the training force can experience conditions similar to an actual battle. In particular, the training effects of the warriors participating in the training can be maximized by establishing an integrated system that utilizes cutting-edge science technologies, such as information communication and computer simulation. This study analyzed the effects of Miles training in the army using scientific techniques targeted at the mid-range Miles. In particular, the effect index for analyzing the training effect was derived from a literature survey and expert opinions. The weight of each effect index was calculated by applying the Swing method. The final training effect was calculated by combining the results of the survey from train-experienced people. The Miles training effect was 2.6 times more effective than previous training without using Miles, and the satisfaction rate with Miles training according to status was high through variance analysis, and the difference was statistically significant.

• Journal of vocational education research
• /
• v.29 no.3
• /
• pp.41-60
• /
• 2010

The purpose of this study was to investigate the relationships between teachers' characteristics and schools' peculiarities and to analyze relevant variables which influence the organization and implementation of the curriculum in specialized industry high schools. Subject were 414 teachers in specialized industry high school. The main result of this study are as follows. First, according to teachers' sex distinction, position, teaching experience, career of industry-related companies, subtle stress difference were found in establishment of education programs, relevance of educational activity, propriety of equipment. Second, in case of schools' subject of establishment, dormitory, specialized types, number of class, type of the sex of recruitment, subtle difference were found in connectivity of graders and subjects, relevance of the organization of education activity, substantial curriculum, push ahead with specialized high school. Third, measures to support development of specialized curriculum arrange sub-items in order of frequency. The way we understand things on vocational education must be settled without delay.

• Journal of Digital Convergence
• /
• v.17 no.6
• /
• pp.219-227
• /
• 2019

This study aims to examine Blockchain research trend using bibliometrics-based network analysis. The data were collected from WoS, Scopus, Korea Citation Index and National science & Technology Information Service, from 2009 to 2018. As results, the number of publications has started increasing rapidly from 2017 and it showed the initial stage of formation of coauthor network. Words often used in the title of the publications were related to application development, controversy and technology development. In addition, the majority of domestic papers are in the subject of social science, while international papers tend to focus on engineering issues. The results of the temporal analysis show that Korean researchers' block chain 3.0 started in 2017 and are rapidly increasing in 2018. The number of citations was associated with publication year in a statistically signifiant way. By examining these research trends, we hope that this paper can be a useful basis for the development of blockchain. Future research is expected to reveal more clearly the knowledge structure and characteristics of blockchain around the world.

• Journal of the Korea Convergence Society
• /
• v.12 no.2
• /
• pp.45-50
• /
• 2021

In this paper, we propose the development of a smart space security device that can be opened and closed remotely using IoT. Existing space security devices can control opening and closing by breaking hardware or only using button devices or replicated keys. The recent COVID-19 crisis has created several applications for non-contact devices. In this study, we propose the development of a small space security device that has the function of unlocking through an app without touching the device. By transferring the control authority to a smartphone, device that cannot be opened or closed by only operating hardware at the user's option. It is convenient and hygienic because it can be opened and closed using an app without touching the locking device. Multiple security is possible because security can be released using an app after user authentication by fingerprint recognition and pattern input on a smartphone. If the user wishes, after using the app security, the security is released by directly touching a button installed in the safe or space or opening it with a key. In addition, by adding an inactive function to the app, it is designed so that the door of the safe cannot be opened when the key is lost or the small safe is lost. This study is expected to be able to effectively expand the security system by applying variously to objects that require security.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.3
• /
• pp.73-86
• /
• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• The Journal of the Korea Contents Association
• /
• v.21 no.8
• /
• pp.32-48
• /
• 2021

This study attends to the phenomenon of major domestic corporations advertising their respective brands on official YouTube channels, reflecting the increasing importance of YouTube as a medium for marketing communication. One hundred vlogs of corporations with an official YouTube channel were content analyzed and their banded content attributes, advertising model's characteristics, advertising appeals, creative strategy, and consumer responses were investigated. Results show that the vlogs of employees were generally formulated with information on their colleagues' tasks and interviews. Findings also demonstrate that the characteristics of the model featured in vlogs had the most significant effects on consumer responses. Amongst the creative strategies, the user image had positive effects. However, branded content characteristics and advertising appeal types were found to have relatively weak effects. This research offers a theoretical understanding of the factors that influence consumer responses to the new type of advertising, corporate vlogs, in terms of branded content characteristics and advertising strategies. From a strategic point, it is anticipated that the reinforcement of user image and the utilization of an attractive model will produce positive responses from consumers.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.345-352
• /
• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.