• Journal of Korea Game Society
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• v.9 no.2
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• pp.125-133
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• 2009

Game artificial intelligence is usually used to provide intelligent and adjusted game environment for user. Previously, it was used for Non-player character(NPC) playing a role of a company or an enemy through collecting and analyzing a user's behaviour. However, it was just mimicking the user's behavior. This paper introduces a method to change game environment by analyzing a user's game behavior. Game behavior data has been used to understand user's game preference. Also, the user's preference was used to provide more active game environment by reflecting decision of geographical features, items and distribution of NPC. For experiment of the suggested method, we utilized a real 2D action game and confirmed the game environment which changing properly according to the user's game play.

• Journal of the HCI Society of Korea
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• v.4 no.2
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• pp.1-8
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• 2009

User-centered design aims to develop device naturally without the interference of user' s conscious and unconscious behavior. Accordingly, designers need to understand their user's requirements, observe user behavior and interaction in the real environment. However, existing observations suggested a vast range of analysis system and observation techniques which are often ambiguous to the designers. Therefore, this research is aimed to propose an observation system for collecting data from user's behavior. In order to do so, we define the components and behavior level within the context of driving.

• The Journal of Korean Association of Computer Education
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• v.8 no.4
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• pp.107-114
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• 2005

While a user is using an adaptive hypermedia system, the user can also use an external application. If the user accesses the information which is related to the contents provided by the adaptive hypermedia system, it can affect a user profile that contains the information about the knowledge or interests of the user. However, the adaptive hypermedia system understands user's behavior based on whether a page is accessed or not and it is difficult for the system to recognize user's behavior that can occur outside the adaptive hypermedia system. In this paper, we propose an approach that can detect user's behavior using a message hooking mechanism so that both user's behavior inside an adaptive hypermedia system and behaviors that occur outside the system can be reflected in a user profile. We analyze user events using a hooking mechanism and update a user profile using an XML parser.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.2
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• pp.67-74
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• 2018

The purpose of this study was to analyze relationship between emotion and behavior with the color images of indoor space in hotels. The study methods evaluation experiments of emotion and behavior with computer graphic images. Study results are as follows: First, The color image according to the indoor space had a significant influence on the user's emotions. Lobby and restaurant had difference in pleasure arousal emotion by color image, and also guest room had difference in pleasure dominance emotion by color image. Second, The color image according to the indoor space had a significant influence on the user's behavior. Also lobby and restaurant had difference in movement hobby eating resting behavior by color image, and guest room had difference in hobby eating resting behavior by color image. Third, As a result of analyzing the effect of user's emotions on the behavior according to the indoor space: In the lobby, the more un-arousal increasing of users, the more hobby resting behavior increasing. And the more dominance increasing of users, the more movement hobby eating resting behavior increasing. In the restaurant, the more dominance increasing of users, the more movement eating hobby behavior increasing. The more arousal increasing of users, the more movement resting behavior increasing. The more un-arousal increasing of users, the more hobby eating behavior increasing. In the guest room, the more un-arousal dominance increasing of users, the more hobby eating resting behavior increasing. And also the more arousal increasing of users, the more movement behavior increasing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.365-371
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• 2021

With the rapid growth in Internet users, web applications are becoming the main target of hackers. Most previous WAFs (Web Application Firewalls) target every single HTTP request packet rather than the overall behavior of the attacker, and are known to be difficult to detect new types of attacks. In this paper, we propose a web attack detection system based on user behavior using machine learning to detect attacks of unknown patterns. In order to define user behavior, we focus on features excluding areas where an attacker can camouflage as a normal user. The experimental results shows that by using the path and query information to define users' behaviors, best results for an accuracy of 99% with Decision forest.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.

• Convergence Security Journal
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• v.22 no.2
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• pp.3-8
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• 2022

Security threats occur from the outside, but more often from the inside. In particular, since the internal user knows about the information service, the security threat damage caused by the internal user is greater. In this environment, the actions of all users accessing information services should be monitored and recorded in real-time. However, the current operating system records only the logs of system and application execution, so there is a limit to monitoring user behavior in real-time. In such a security environment, damage may occur due to user's unauthorized actions. To solve this problem, this study proposes an architecture that monitors user behavior in real-time in a Linux environment. As a result of verifying the function to confirm the effectiveness of the proposed architecture, the console input values and output angles of all users who have access to the operating system are monitored in real-time and stored. Although the performance of the proposed architecture is somewhat slower than the identification and authentication functions provided by the operating system, it was confirmed that the performance was not at a level that users would recognize, and thus it was judged to be sufficiently effective. However, since this study focuses on monitoring the console behavior, it is impossible to monitor the behavior of user applications running in the background, so additional research is needed.

• Journal of Information Science Theory and Practice
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• v.6 no.3
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• pp.25-36
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• 2018

Anonymity plays an increasingly important role on social media. This is reflected by more and more applications enabling anonymous interactions. However, do social media users behave different when they are anonymous? In our research, we investigated social media services meant for solely anonymous use (Jodel) and for widely spread non-anonymous sharing of pictures and videos (Instagram). This study examines the impact of anonymity on the behavior of users on Jodel compared to their non-anonymous use of Instagram as well as the differences between the user types: producer, consumer, and participant. Our approach is based on the uses and gratifications theory (U>) by E. Katz, specifically on the sought gratifications (motivations) of self-presentation, information, socialization, and entertainment. Since Jodel is mostly used in Germany, we developed an online survey in German. The questions addressed the three different user types and were subdivided according to the four motivation categories of the U>. In total 664 test persons completed the questionnaire. The results show that anonymity indeed influences users' usage behavior depending on user types and different U> categories.