• The Journal of Information Systems
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• v.28 no.1
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• pp.97-114
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• 2019

Purpose The purpose of this study is to deduct the factors for explaining the economic behavior of an Internet user who provides personal information notwithstanding the concern about an invasion of privacy based on the Information Privacy Calculus Theory and Communication Privacy Management Theory. Design/methodology/approach This study made a design of the research model by integrating the factors deducted from the computation theory of information privacy with the factors deducted from the management theory of communication privacy on the basis of the Dual-Process Theory. Findings According to the empirical analysis result, this study confirmed that the Privacy Concern about forms through the Perceived Privacy Risk derived from the Disposition to value Privacy. In addition, this study confirmed that the behavior of an Internet user involved in personal information offering occurs due to the Perceived Benefits contradicting the Privacy Concern.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.717-723
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• 2019

Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Management Science and Financial Engineering
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• v.16 no.2
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• pp.29-38
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• 2010

As the Internet is more embedded in people's lives, Internet users draw on new Internet applications to express themselves through "user-created content (UCC)." In addition, there is a noticeable shift from text-centered contents mainly posted on bulletin boards to multimedia contents such as images and videos on UCC web sites. The changes require different way of recommendations comparing to traditional products or contents recommendation on the Internet. This paper aims to design UCC recommendation methods with user behavior data and contents metadata such as tags and titles, and compare performances of the suggested methods. Real web logs data of a major Korean video UCC site was used to empirical experiments. The results of the experiments show that collaborative filtering technique based on similarity of UCC customers' preferences performs better than other content-based recommendation methods based on tag information and content metadata.

• Journal of the Korean housing association
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• v.18 no.2
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• pp.129-136
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• 2007

This study, as a case study of four university dormitories in Gwangju, examined the situation of interior space and conducted a quantitative survey investigating importance about the interior space and assessment factors of residential environment on users. Then user satisfaction at the assessment factors of residential environment was analyzed to draw problems and explore improvement. The results showed the following problems that should be resolved: Bedrooms should increase the area of convex space; toilets and bathrooms should be changed into a cluster type at least in common use by floor; rest space should reinforce heating and cooling system; robby space should become mixed space for various user's behavior; private fitness rooms should be planned as group fitness space; private libraries should reinforce lighting facilities; and computer rooms should complement and improve HVAC.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.4
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• pp.161-168
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• 2003

We discuss the security of two famous password authenticated key exchange protocols, EKE2 and PAK. We introduce ′insider assisted attack′ Based on this assumption we point out weakness of the security of EKE2 and PAK protocols. More precisely, when the legitimate user wants to find other user′s password, called "insider-assisted attacker", the attacker can find out many ephemeral secrets of the server and then after monitoring on line other legitimate user and snatching some messages, he can guess a valid password of the user using the previous information. Of course for this kind of attack there are some constraints. Here we present a full description of the attack and point out that on the formal model, one should be very careful in describing the adversary′s behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1260-1272
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• 2024

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.37-54
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• 2010

The information system that preserves and presents information collections, records, processes, retrievals, is applied in various fields recently and is supporting man's many activities. Conventional information systems are based on the reactive interaction model. Such reactive systems respond to only specific instructions, i.e. the defined commands, from the user. To go beyond the reactive interaction, it is necessary that the interactive dynamic interaction based information system which understands human's action and intention autonomously and then provides sensible information adapted to the user. Therefore, we propose a Gaze Mirroring-based intelligent information system for making user's latent interest using the internal state estimation methods based on the interactive dynamic interaction. Then, the proposed Gaze Mirroring method is that an anthropomorphic agent(avatar) actively established the joint attention with the user by imitating user's eye-gaze behavior. We verify that the Gaze Mirroring can elicit the user's behavior reflecting the latent interestand contribute to improving the accuracy of interest estimation. We also have confidence that the Gaze Mirroring promotes the self-awareness of interest. Such a Gaze Mirroring-based intelligent information system also provides suitable information to user by making user's latent interest using the internal state estimation.

• The Journal of Information Systems
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• v.29 no.1
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• pp.201-224
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• 2020

Purpose The study focuses on the physical activity behavior change effect of smart wristband, which is the most popular type of fitness tracker nowadays. The purpose of the research is to investigate how people's workout behavior may change after wearing a smart band and examine what kind of role persuasive design plays in behavior change. Design/Methodology/Approach This research employed an experimental study to examine whether the user's workout behaviors changed after using wristband from the "Behavior Wizard" perspective. A representative smart wristband from a major vendor was selected as the objects of experimental study. In the experiment, by comparing users' workout behavior before and after using the wristband, behavior changes of all the experiment participants were classified into one of the 15 behavior change types. Users perceived persuasive design characteristics were measured and group differences were tested among different behavior change groups. Findings This research found that nearly half of the participants changed their workout behavior while half retained their workout status or no exercise status. Half of the participants who did not do exercise in their spare time started walking in the experiment. Results also showed that participants who started working out perceived higher levels of persuasive design devised into the smart band than participants who preserved no exercise status, except for facilitation and reward strategies. Participants who retained workout and those who increased workout frequency perceived no difference in smart band persuasive design.