• Journal of Information Management
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• v.40 no.1
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• pp.183-197
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• 2009

With development of the internet, internet shopping is taking its place as one of digitalization industries transcending time and space beyond the scope of commercial activities as the means of goods sales and purchase. We studied about the relations of customer value, environment of internet shopping mall, customer satisfaction and loyalty. Customer value is customers' subjective evaluation, which is formed after their purchasing and consuming. Customer satisfaction can be characterized as post-purchase evaluation of product quality given pre-purchase expectations. Customer loyalty is a potentiality or ensure of durative relationship between customer and enterprises. Customer satisfaction functions as an antecedent of customer loyalty, while customer value does customer satisfaction. It prevents customer churn and consolidates retention, thereby constituting an important cause of customer loyalty. This study shows that customer value, environment of internet shopping mall and customer satisfaction are each found to have a direct effect on customer loyalty. The results provide empirical support for relation between customer satisfaction and loyalty. To increase customer satisfaction and customer loyalty in internet shopping mall is the primary purpose of this study. We believe that only high quality based customer programs accompanied by well designed loyalty programs can be effective in increasing customer retention.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.9
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• pp.8-19
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• 2002

The Dublin Core element set has been developed over the past years as an open, consensus-building metadata from many user communities. Wider adoption in many countries made this metadata a major resource discovery standard on the internet. The version 1.1 of the Dublin Core element set was adopted as CEN Workshop Agreement 13874 in Europe, and also ratified under the auspices of the National Information Standards Organization in US as ANSI Standard Z39.85. This report summarizes the standardization activities that have taken place in the Dublin Core Metadata Initiative for the past years.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.749-766
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• 2021

Cyber powers around the world are conducting cyber information-gathering activities in cyberspace, a global domain within the Internet-based information environment. Accordingly, it is imperative to obtain the latest information through the cyber intelligence preparation of the battlefield (IPB) process to prepare for future cyber operations. Research utilizing the cyber battlefield visualization method for effective cyber IPB and situation awareness aims to minimize uncertainty in the cyber battlefield and enable command control and determination by commanders. This paper designed architecture by classifying cyberspace into a physical, logical network layer and cyber persona layer to visualize the cyber battlefield using BGP archive data, which is comprised of BGP connection information data of routers around the world. To implement the architecture, BGP archive data was analyzed and pre-processed, and cyberspace was implemented in the form of a Di-Graph. Information products that can be obtained through visualization were classified for each layer of the cyberspace, and a visualization method was proposed for performing cyber IPB. Through this, we analyzed actual North Korea's BGP and OSINT data to implement North Korea's cyber battlefield centered on the Internet network in the form of a prototype. In the future, we will implement a prototype architecture based on Elastic Stack.

• Korean Journal of Human Ecology
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• v.23 no.3
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• pp.557-569
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• 2014

As the number of Chinese students in Korea has significantly increased, the problems of students' life and academic achievement has appeared obviously. Utilizing an analysis on the relationship between the life satisfaction of Chinese students in Korea and the degree of internet addiction, the purposes of this research are to improve the quality of Chinese students' life and to contribute to the growth of Chinese students in Korea. A questionnaire-type survey was conducted on 350 Chinese college students in Daegu. The results of this study are as follows: First, the used time on internet games, chat and on-line TV differs from gender, the duration of internet games and their academic performance. Second, there are significant differences by gender, Korean ability, and academic performance in the life satisfaction of Chinese students in Korea. Third, Chinese college students in Korea are more addicted in the internet than Korean or Chinese college students at their own country. Fourth, the longer playing online game, the longer watching online TV, the less communicating with the Koreans, and the less satisfying with non -study related activities, the more Chinese college students of studying in Korea are addicted in the internet.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.489-501
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• 2016

As interest in the Internet increases, related technologies are also quickly progressing. As smart devices become more widely used, interest is growing in words are missing here like "improving the" or "figuring out how to use the" future Internet to resolve the fundamental issues of transmission quality and security. The future Internet is being studied to improve the limits of existing Internet structures and to reflect new requirements. In particular, research on words are missing here like "finding new forms of" or "applying new forms of" or "studying various types of" or "finding ways to provide more" reliable communication to connect the Internet to various services is in demand. In this paper, we analyze the security threats caused by malicious activities in the future Internet and propose a human behavior analysis-based security service model for malware detection and intrusion prevention to provide more reliable communication. Our proposed service model provides high reliability services by responding to security threats by detecting various malware intrusions and protocol authentications based on human behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1818-1832
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• 2022

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1445-1462
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• 2012

While botnets are used for various malicious activities, it is well known that they are widely used for email spam. Though the spam filtering systems currently in use block IPs that send email spam, simply blocking the IPs of zombie PCs participating in a botnet is not enough to prevent the spamming activities of the botnet because these IPs can easily be changed or manipulated. This IP blocking is also insufficient to prevent crimes other than spamming, as the botnet can be simultaneously used for multiple purposes. For this reason, we propose a system that detects botnets and zombie PCs based on email spam analysis. This study introduces the concept of "group pollution level" - the degree to which a certain spam group is suspected of being a botnet - and "IP pollution level" - the degree to which a certain IP in the spam group is suspected of being a zombie PC. Such concepts are applied in our system that detects botnets and zombie PCs by grouping spam mails based on the URL links or attachments contained, and by assessing the pollution level of each group and each IP address. For empirical testing, we used email spam data collected in an "email spam trap system" - Korea's national spam collection system. Our proposed system detected 203 botnets and 18,283 zombie PCs in a day and these zombie PCs sent about 70% of all the spam messages in our analysis. This shows the effectiveness of detecting zombie PCs by email spam analysis, and the possibility of a dramatic reduction in email spam by taking countermeasure against these botnets and zombie PCs.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.27-36
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• 2022

Metabus, currently represented by Second Life on the Internet, is the next-generation 3DCG Internet world in which the 2D Internet world has evolved, and has grown as a new ICT culture of mankind that can replace real society with virtual society. As such, the reason why the world of metabus has rapidly expanded is that the era of 3D Internet has arrived due to the evolution of the Internet, which only used information, and the spread of 5G communication in user-participating WEB. However, there are many situations in which laws do not exist in this virtual world and various illegal acts occur. As the Internet culture developed earlier, illegal activities by users began to appear, and as the legal responsibility of Internet providers was discussed, mankind quickly passed the Millennium Copyright Act or introduced new copyright protection measures such as technical protection, transmission rights, and rights management information. Therefore, this paper reviews and studies how to accept and further grow this new metabus culture, including the viewpoint of intellectual property.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.