• 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.

• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.56-57
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• 2002

• Design Convergence Study
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• v.16 no.5
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• pp.203-217
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• 2017

This study proposes and demonstrates the value of user - centered design methodology based on linguistic analysis. The results of the proposed user behavioural analysis framework suggested that the syntactic structure between the sentence structure and its components could be a logical basis for explaining the user's situation and behavior. Based on this, the definitions and classifications of user interactions and user contexts were conducted in a microscopically context. User behavior has also been established to identify pattern structures of purposeful nature and constitutes a user behavior sequence that prioritizes them. Next, the User Experience Analysis Framework was derived by defining the relationship between User Behavior and User Behavior and User Context and User Context. To verify the framework of the framework, a professional assessment was conducted to conduct a review of the user's experience and conduct a study of the framework of the framework and conduct of the framework of the framework of the framework and practical utility of the framework. Through this, it was possible to identify the value of the qualitative and quantitative framework of the framework and the future direction of development.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.523-538
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• 2018

Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patterns in how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed to another user, it analyzes the input pattern to authenticate the user; hence, it can compensate for the drawbacks of knowledge-based (what you know) authentication. However, users' input patterns are not always fixed, and each user's touch method is different. Therefore, there are limitations to extracting the same features for all users to create a user's pattern and perform authentication. In this study, we perform experiments to examine the changes in user authentication performance when using feature vectors customized for each user versus using all features. User customized features show a mean improvement of over 6% in error equal rate, as compared to when all features are used.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.64-71
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• 2006

Electronic mail (E-mail) is an integral part of communication for the recent Internet users. However, e-mail has also come to serve as a means to support flood of unwanted spam mails and junk mails having bad purposes. This paper was conducted in order to develop an intelligent e-mail system using user behavior pattern that can prevent these unnecessary information and enable the user to enjoy communication via e-mail in a cleaner environment. The concentrated analysis of the user behavior in terms of using e-mail functions has resulted in better classification between unnecessary and necessary information, thereby facilitating faster disposal of spam mails.

• Review of Korea Contents Association
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• v.18 no.1
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• pp.12-17
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• 2020

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.173-178
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• 2008

Due to a rapid increase in demand for bigger-screen-equipped mobile phones in recent years, many big-name-manufactures have been releasing touch-screen-enabled devices. In this paper, various touch-screen-input methods have been summarized into 6 different categories. How? By tracing each user's finger print path, user's input pattern and behavior have been carefully recorded and analyzed. Through this analysis, what to be considered before designing UI is presented in great details.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.805-812
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• 2005

Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.361-371
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• 2017

The increase in user data stored in the device implies the increase in threats of users' sensitive data. Currently, smartphone authentication mechanisms such as Pattern Lock, fingerprint recognition are widely used. Although, there exist disadvantages of inconvenience use and dependence that users need to depend on their own memory. User behavior based authentication mechanism have advantages of high convenience by offering continuous authentication when using the mobile device. However, these mechanisms show limitations on low accuracy of authentication and there are researches to improve the accuracy. This paper proposes improved authentication mechanism that uses user's smartphone application usage pattern which has not considered on earlier studies. Also, we analyze performance of proposed mechanism with collected datasets from actual use of smartphone applications.