• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.369-376
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• 2019

This paper studies desirable form of future e-government in terms of intelligent government research in response to new intelligent cyber security services in the fourth industrial revolution. Also, the strategic planning of the future e-government has been contemplated in terms of the centralization and intellectualization which are significant characteristics of the fourth industrial revolution. The new system construction which is applied with security analysis technology using big data through advanced relationship analysis is suggested in the paper. The establishment of the system, such as SIEM(Security Information & Event Management), which anticipatively detects security threat by using log information through big data analysis is suggested in the paper. Once the suggested system is materialized, it will be possible to expand big data object, allow centralization in terms of e-government security in the fourth industrial revolution, boost data process, speed and follow-up response, which allows the system to function anticipatively.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.209-224
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• 2006

Hierarchy which is often named as the tree-structure is used to reduce complexity and show primitive structures of complicated information. This paper aims at explaining information-visualization methods using hierarchies in multimedia domains and prospecting the possible applications by examining how they affect the user's tasks involved in information-seeking activities. As a result, four types of information visualization methods named Treemap, Hyperbolic, Cone Tree and DOI Tree employed in multimedia domain, are presented and pros and cons of each method are explained in this paper. Another important part is defining the core tasks and other related-tasks in information-seeking activities, such as, overview, zoom, filter, details-on-demand, relate, history, and extract. Followings are major findings. Treemap uses 'overview' as the core task, which makes user to gain a overall meaning of the whole information cluster. Hyperbolic and DOI Tree apply 'Boom' task through the function of focus+context or by the function of meaningful scaling to magnify or downsize each node. Cone Tree, also, makes the information organizer to classify the patterns of information acquired in the process of users' information-seeking activities by using 'extract' task. Through this study, it is finally found out that the information-visualization methods using hierarchies in multimedia domains should incorporate the wide variety of functional needs related to users' information-seeking behaviors beyond the visual representation of information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.404-412
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• 2011

More recently, variety RFID (Radio Frequency Identification) readers are produced by RFID equipment manufactures. Although a transmission standard instituted by EPCglobal is proposed for data transmission between the RFID readers and tags, other RFID reader protocols and the communication connection methods are be in use in other RFID companies. To replace or add the RFID readers of an RFID system, the developers should make changes to the core of the application and/or middleware. In this paper, we propose an RFID reader framework which can manage RFID readers without having to make changes the code of the application in environment with the growing number of heterogeneous RFID readers.This framework that sits on the layer between the RFID readers and the applications provides transparency to the RFID readers. Additionally, it can be used for monitoring the state and the property of all connected RFID, and for recording the RFID tag event logs and system logs. By using this framework, heterogeneous readers can be replaced and added without writing additional code in the applications. Consequently the readers can be easily managed and controlled by the RFID system administrator.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.