• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.55-66
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• 2012

This paper presents a dual modeling method that simulates the graphic and haptic behavior of a volumetric deformable object and conveys the behavior to a human operator. Although conventional modeling methods (a mass-spring model and a finite element method) are suitable for the real-time computation of an object's deformation, it is not easy to compute the haptic behavior of a volumetric deformable object with the conventional modeling method in real-time (within a 1kHz) due to a computational burden. Previously, we proposed a fast volume haptic rendering method based on the S-chain model that can compute the deformation of a volumetric non-rigid object and its haptic feedback in real-time. When the S-chain model represents the object, the haptic feeling is realistic, whereas the graphical results of the deformed shape look linear. In order to improve the graphic and haptic behavior at the same time, we propose a dual modeling framework in which a volumetric haptic model and a surface graphical model coexist. In order to inspect the graphic and haptic behavior of objects represented by the proposed dual model, experiments are conducted with volumetric objects consisting of about 20,000 nodes at a haptic update rate of 1000Hz and a graphic update rate of 30Hz. We also conduct human factor studies to show that the haptic and graphic behavior from our model is realistic. Our experiments verify that our model provides a realistic haptic and graphic feeling to users in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.719-745
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• 2015

Social networking services provide individuals with an easy approach for exchanging messages with others based on interpersonal relationships. However, why individuals spread marketer-generated content (MGC) in their online social circles remains unclear. Therefore, we develop a theoretical model to examine how social capital, intrinsic motivations, personal perceptions, past behavior, and personal traits influence MGC sharing behavior of social media users in micro-blogging context. Data collected from 319 social networking users support the proposed model. The results from partial least squares analyses show that enjoyment, perceived control, and outcome expectations are significant indicators of individual's MGC sharing intention in the social broadcasting environment. Results also suggest that social capital, users' intention, and past behavior positively influence the MGC sharing behavior of users. Moreover, individual prevention pride exhibits a significant interaction effect on the relationships between users' MGC sharing and its antecedents. Implications for research and practice are discussed.

• Management & Information Systems Review
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• v.1
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• pp.193-228
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• 1997

Behaviors of organizational members can be classified as two types. One is behavior required to perform formally given job. The other is a various kind of behavior taken voluntarily but not required directly and formally to perform job. The former can be called as in-job behavior while the latter can be called as extra-job behavior. Many organizational scientists so far, have focused on investigating in-job behavior. Yet, from a decade, organizational researchers have recognized that in-job behavior alone can not explain sufficiently job performance or organizational effectiveness Thus. they have paid attention to extra-job behavior, which is generally called as organizational citizenship behavior. Existing studies of organizational citizenship behavior have three types : First type is studying the concept and components of organizational citizenship behavior. Second tope is studying the determinants of organizational citizen-ship behavior and relationships with it. And third type is studying relatioships between organizational citizenship behavior and job performance. This study, therefore, have purposes as follows : Firstly, this study designs a comprehensive model in the below figure and generates inclusive hypotheses about relationships among antecedents, intermediate factors, and the components of organizational citizenship behavior. Secondly, this study investigating empirically such relationships and draws a picture of mediation roles of the intermediate variables. To design the model and generate the hypotheses, this study conducted a comprehensive literature survey on organizational citizenship behavior. To test the hypotheses, this study collected data from 847 employees at 12 large genral hospitals in Pusan area through a questionnaire survey and conducted the three step mediated regression analysis using the SAS-PC Package.

• ETRI Journal
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• v.35 no.6
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• pp.1058-1067
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• 2013

An approach for game bot detection in massively multiplayer online role-playing games (MMORPGs) based on the analysis of game playing behavior is proposed. Since MMORPGs are large-scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. To cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.117-135
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• 2004

Since human decision making behavior is likely to follow nonlinear strategy, it is conjectured that the human decision making behavior can be modeled better by nonlinear models than by linear models. All that linear models can do is to approximate rather than model the decision behavior. This study attempts to test this conjecture by analyzing human decision making behavior and combining the results of the analysis with predictive performance of both linear models and nonlinear models. In this way, this study can examine the relationship between the predictive performance of models and the existence of valid nonlinear strategy in decision making behavior. This study finds that the existence of nonlinear strategy in decision making behavior is highly correlated with the validity of the decision (or the human experts). The second finding concerns the significant correlations between the model performance and the existence of valid nonlinear strategy which is detected by Lens Model. The third finding is that as stronger the valid nonlinear strategy becomes, the better nonlinear models predict significantly than linear models. The results of this study bring an important concept, validity of nonlinear strategy, to modeling human experts. The inclusion of the concept indicates that the prior analysis of human judgement may lead to the selection of proper modeling algorithm. In addition, lens Model Analysis is proved to be useful in examining the valid nonlinearity in human decision behavior.

• The Journal of Information Systems
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• v.32 no.3
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• pp.85-116
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• 2023

Purpose The importance of data as a key resource of the intelligence revolution is being highlighted, among all those phenomena MyData is attracting attention as a key concept by organizations and individuals that eventually leads the data economy. In this regard, this study was started to contribute to the successful settlement and continuous growth of the domestic MyData industry, which has just entered the system. Design/methodology/approach To develop and test all proposed casual relationships within the research model, we used the Value-Attitude-Behavior(VAB) model as a basic framework. A total of 385 copies were used for the final analysis, and for SPSS 25.0, MS-Excel 2016, and AMOS 24.0 to summarize respondent demographic characteristics, measurement model, and structural model. Findings Findings show that all proposed hypotheses were supported with the exception of the moderating effect of organizational information transparency between data controllability and perceived value, and between data controllability and attitude toward MyData service.

• The Journal of Information Systems
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• v.17 no.2
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• pp.73-94
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• 2008

Knowledge is a fundamental assets, therefore, the ability to create, acquire, integrate, and share knowledge has emerged as a fundamental organizational capability(Sambamurthy and Subramani, 2005). This apaper reports the results of an empirical study investigating the factors of acceptance and the moderating role of resistance in Knowledge Management Systems(KMS). The research model is based on the theory of planned behavior(TPB) and technology acceptance model(TAM). It includes the perceived usefulness instead of attitude, subjective norm, perceived behavior control and intention of acceptance of KMS. Also, three external variables namely task-technology fit, organizational support, and perceived rewards are added. In the research model, all hypothrses of the baseline model and the moderating effects of resistance were found to be significant. The authors also of fred several implications based chi the findings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.659-679
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• 2015

Distributed applications are composed of multiple nodes, which exchange information with individual nodes through message passing. Compared with traditional applications, distributed applications have more complex behavior patterns because a large number of interactions and concurrent behaviors exist among their distributed nodes. Thus, it is difficult to detect anomalous behaviors and determine the location and scope of abnormal nodes, and some attacks and misuse cannot be detected. To address this problem, we introduce a method for detecting anomalous behaviors based on process algebra. We specify the architecture of the behavior detection model and the detection algorithm. The anomalous behavior detection and analysis demonstrate that our method is a good discriminator between normal and anomalous behavior characteristics of distributed applications. Performance evaluation shows that the proposed method enhances efficiency without security degradation.