• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4157-4175
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• 2020

Moving target defense, as a 'game-changing' security technique for network warfare, realizes proactive defense by increasing network dynamics, uncertainty and redundancy. How to select the best countermeasure from the candidate countermeasures to maximize defense payoff becomes one of the core issues. In order to improve the dynamic analysis for existing decision-making, a novel approach of selecting the optimal countermeasure using game theory is proposed. Based on the signal game theory, a multi-stage adversary model for dynamic defense is established. Afterwards, the payoffs of candidate attack-defense strategies are quantified from the viewpoint of attack surface transfer. Then the perfect Bayesian equilibrium is calculated. The inference of attacker type is presented through signal reception and recognition. Finally the countermeasure for selecting optimal defense strategy is designed on the tradeoff between defense cost and benefit for dynamic network. A case study of attack-defense confrontation in small-scale LAN shows that the proposed approach is correct and efficient.

• ETRI Journal
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• v.41 no.5
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• pp.585-598
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• 2019

A cyber-physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well-funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game-theoretical model considering both low- and high-interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.251-256
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• 2008

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.197-207
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• 2015

In baseball game, the hitting ability of batter is frequently assessed by a batting average, a run batted in, a home run, a run scored, an on-base percentage, etc. Recently, more comprehensive indicators such as OPS, ISO, SECA, TA, RC and XR are often used. But, these measures generally shows large deviations since they are calculated from the data for a certain period of time, and they are not an estimate of a population parameter, either. In this paper, we will presume the pure hitting ability of the korea professional baseball players as a parameter which is depend upon at bat. We will estimate the parameter by using the Bayesian method.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.517-524
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• 2017

Cyber security is an important issue in the field of nuclear engineering because nuclear facilities use digital equipment and digital systems that can lead to serious hazards in the event of an accident. Regulatory agencies worldwide have announced guidelines for cyber security related to nuclear issues, including U.S. NRC Regulatory Guide 5.71. It is important to evaluate cyber security risk in accordance with these regulatory guides. In this study, we propose a cyber security risk evaluation model for nuclear instrumentation and control systems using a Bayesian network and event trees. As it is difficult to perform penetration tests on the systems, the evaluation model can inform research on cyber threats to cyber security systems for nuclear facilities through the use of prior and posterior information and backpropagation calculations. Furthermore, we suggest a methodology for the application of analytical results from the Bayesian network model to an event tree model, which is a probabilistic safety assessment method. The proposed method will provide insight into safety and cyber security risks.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.107-111
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• 2005

When a pedagogical agent system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. In this paper, the student model for interactive edutainment applications is proposed. This model is based on Bayesian Networks to expose constructs and parameters of rules and propositions pertaining to game and problem solving activities. This student model could be utilized as the emotion generation model for student and agent as well.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.422-427
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• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.567-571
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• 2009

Sports game broadcasting system uses multiple video cameras in order to offer exciting and dynamic scenes for the TV audiences. Since, however, the traditional broadcasting system edits the multiple views into a static video stream, it is difficult to provide the intelligent broadcasting service that summarizes or retrieves specific scenes or events based on the user preference. In this paper, we propose the summarization and retrieval system for the soccer videos based on multiple cameras. It extracts the highlights such as shot on goal, crossing, foul, and set piece using dynamic Bayesian network based on soccer players' primitive behaviors annotated on videos, and selects a proper view for each highlight according to its type. The proposed system, therefore, offers users the highlight summarization or preferred view selection, and can provide personalized broadcasting services by considering the user's preference.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.461-468
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• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.