• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.355-357
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• 2016

Korea Information Security Industry growth rate (7.1%) declined compared to the previous three years growth (15%). The government has announced a 2020 K-ICT security. According to the policy of promoting the competitiveness of industry consolidation and data protection enable entrepreneurship, job creation, there is a need for the ICT industry in conjunction with cyber security cluster composition. In this paper, I research for cybersecurity cluster. Investigate a successful cyber clusters of foreign and analyzed. In addition, I analysis of existing cluster in the domestic and identify the problem. Consequently, building a cyber-research cluster and the expected effects of cyber-research and on how to operate the cluster.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.109-123
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• 2024

The purpose of this study is to identify strategic insights for cyber universities to secure a competitive advantage based on market analysis grounded in customer needs and motivations. As a research method, we surveyed and analyzed college students using conjoint analysis, identified the importance of cyber university components, estimated the utility of each detailed level, and identified the configuration of cyber universities most preferred by potential customers. In the study results, the importance of attributes that appeared by analyzing all respondents was in the order of 'expected ourcoms after graduation', 'department characteristic', 'cyber university name', and 'learning management style'. Cluster analysis was performed, divided into two groups, and conjoint analysis was performed. For Cluster 1, the importance values of the components were 'expected outcomes after graduation,' 'learning management style,' 'cyber university name,' and 'department characteristics,' in that order. For Cluster 2, the importance values were 'expected outcomes after graduation,' 'department characteristics,' 'cyber university name,' and 'learning management style,' in that order. As an application of the research, As an application of the study, it is suggested that analyzing the preferences of potential customers in the entire group is not accurate; therefore, segmenting the groups for analysis and strategy formulation can be useful.

• International Commerce and Information Review
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• v.9 no.4
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• pp.217-239
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• 2007

We want to know the key factors which are important to make any cluster grow up. First, we make four cluster types which are national policy type, local recruit type, local network type, and existing industry transforming type. Second, We selected Austin case and Kyushu case as two local recruit type clusters. Third, this paper looks into the dynamics of two clusters. Forth, we arrive at the conclusion including the policy implication for Daegu technopolis.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.3_4
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• pp.441-451
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• 2003

The purpose of this study was to find out evaluative criteria of cyber store toward shopping orientation, purchasing experience and demographic factors of consumers to visit cyber store. This study surveyed consumers who have an experience of using cyber store and judgment sampling was used. The respondents were 240 men and women living in the metropolitan area of Seoul and Gyeonggi province. Research method was measured by clothing shopping orientation (utilitarian and hedonic factors), cyber store evaluation criteria (convenience, quality & trust and product character factors), purchasing experience(existent and nonexistent) and demographic factors. For data analysis, descriptive statistics, factor analysis, cluster analysis, ANOVA, t-test, Duncan test, and reliability analysis were conducted. The results were as follows: 1. Evaluative criteria of cyber store were considered importantly in order of possibility of exchange and refund, stability of personal information security, substance of quality degree of product and service and store. 2. Among shopping orientation groups, there were significant differences in all evaluative criteria of cyber store: convenience, quality & trust and product character of store. 3. Among groups toward existence and nonexistence in a purchasing experience, there were significant differences in convenience and quality & trust of store. 4. Among Demographic factors(gender, job, education and income) there were significant differences in convenience and quality & trust of store.

• Journal of the Korean Home Economics Association
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• v.42 no.11
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• pp.223-235
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• 2004

This study investigated the integrated effects of family and school factors on the cyber behavior of adolescents. Specifically, the purposes of this study were to categorize adolescents into groups by family and school factors and to find investigate differences among the groups regarding cyber behavior (internet use, internet purpose, and internet experience). no study distributed the questionnaires to middle and high school adolescent students of five representative cities in South Korea. The total respondents were 2240 (960 from Seoul/kyongki, and 320 each from Taegu, Pusan, Kwangiu, and Taejon). The response rate w3s 98.7%. no data were analyzed by factor analysis, cluster analysis, ANOVA, and Duncan test. The results showed that Korean adolescents were segmented into four groups (family preference/school preference group, family dissatisfaction/teacher dissatisfaction group, family average/school average group, family average/peer dissatisfaction group). The four groups were significantly different in regard to cyber behavior. For example, the family dissatisfaction/teacher dissatisfaction group u%d internet to relieve stress and used communication more than the other groups. Also, the group had more diverse cyber behavior including internet addiction. The implications of the study were further discussed.

• Journal of the Korean Home Economics Association
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• v.43 no.11 s.213
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• pp.153-164
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• 2005

The purpose of this study was to examine the effect of relationship commitment on the female customer's future behavioral intention in relation to the criteria of evaluating cyber stores in internet shopping malls. This study used questionnaire and judgment sampling to survey consumers who have bought product in internet shopping malls. The respondents were 329 women from their twenties to fifties. The data were analyzed by factor analysis, cluster analysis, ANOVA, regression and Duncan test. The results were as follows: 1. The evaluative criteria of cyber stores were product characteristics of the store, convenience and trust, and promotion and information provision. 2. There were significant differences in relationship commitment among groups according to differences of cyber store evaluation criteria. 3. The dimensions of relationship commitment were affective commitment, calculus commitment and normative commitment. 4. Relationship commitment was found to have a significant effect on the customer's future behavioral intention. Especially, affective commitment was shown to have a significant effect on the future behavioral intention.

• The KIPS Transactions:PartA
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• v.14A no.1 s.105
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• pp.63-72
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• 2007

This paper introduces a load-balancing algorithm focusing on distributing web requests evenly into the web cluster servers. The load-balancing algorithms based on conventional periodic load-information update mechanism are not suitable for dynamic page applications, which are common in Cyber Foraging services, due to the problems caused by periodic synchronized load-information updating and the difficulties of work load estimation caused by embedded executing scripts of dynamic pages. Update-on-Finish algorithm solves this problem by using non-periodic load-update mechanism, and the web switch knows the servers' real load information only after their reporting and then distributes new loads according to the new load-information table, however it results in much communication overhead. Our proposed mechanism improve update-on-finish algorithm by using K-Percents-Finish mechanism and thus largely reduce the communication overhead. Furthermore, we consider the different capabilities of servers with a threshold Ti value and propose a load-balancing algorithm for servers with various capabilities. Simulation results show that the proposed K-Percents-Finish Reporting mechanism can at least reduce 50% communication overhead than update-on-finish approach while sustaining better load balancing performance than periodic mechanisms in related work.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.767-780
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• 2015

In recent years, as smart phone penetration rate is growing explosively, new forms of cyber crime data is poured out beyond the limits of management system for cyber crime investigation. These new forms of data are collected and stored in police station but, some of data are not systematically managed. As a result, investigators sometimes miss the hidden data which can be critical for a case. Crime data is usually generated by computer which produces complex and huge data and records many logs automatically, so it is necessary to simplify a collected data and cluster by crime pattern. In this paper, we categorize all kinds of cyber crime and simplify crime database and extract critical clues relative to other cases. Through data mining and network-visualization, we found there is correlation between clues of a case. From this result, we conclude cyber crime data mining helps crime prevention, early blocking and increasing the efficiency of the investigation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.