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

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.6
• /
• pp.1575-1582
• /
• 1998

In this paper. we design and evaluate a cooperating model that increase problem solving ability by selecting proper cooperating partners under changing situation. In this model, to decide cooperation direction and extent, we have used a payoff function and then divided the group into two parts, cooperation part and non-cooperation part. To control these reconfigured groups at group level, group intention is used as a control media instead of existing data and goal. Group intention is abstractive and comprehensive and represents collection of strategies. Group intention is changed based on resources, information, and cooperation situation on group intention hierarchy. Two layered control is possible : first constraint with group intentions(group level) and then select a strategy under the constraint. These approaches are tested and evaluated on pursuit game testbed.

• Korean Management Science Review
• /
• v.21 no.2
• /
• pp.145-165
• /
• 2004

Frequently. lots of organizations have experienced the value discrepancy between the expected value and the realized value from IS (information systems) investments. Being positive or negative the difference is. however, the existence of discrepancy itself is an evidence of less-than-sound management and measurement of IS projects. Analyzing the factors that cause such discrepancy has become an issue of scrutiny both in academia and in practice. We model which factors. as predictors, will affect the value discrepancy, as dependent variables. in IS investment. This research will establish and examine the research model. the validity of category classification of value discrepancy factors and the perceptual level of IS value discrepancy by survey research. As a result of the survey research. the strategic alignment. the proper system design for staffs. the project planning capability. and interdepartmental task cooperation are perceived as the factors that significantly affect the value discrepancy. And known as IS success factors such as the managerial support, the change management, the standardized process. and the competitive investment are not significant factors. The research findings will provide and emphasize useful implications which factors should be deliberately investigated in IS investment both for practices considering IS deployment and for academia.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.1
• /
• pp.120-126
• /
• 2014

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.52 no.9
• /
• pp.542-549
• /
• 2003

In this paper, a novel approach to generator maintenance scheduling strategy in competitive electricity markets based on non-cooperative dynamic game theory is presented. The main contribution of this study can be considered to develop a game-theoretic framework for analyzing strategic behaviors of generating companies (Gencos) from the standpoints of the generator maintenance-scheduling problem (GMP) game. To obtain the equilibrium solution for the GMP game, the GMP problem is formulated as a dynamic non-cooperative game with complete information. In the proposed game, the players correspond to the profit-maximizing individual Gencos, and the payoff of each player is defined as the profits from the energy market. The optimal maintenance schedule is defined by subgame perfect equilibrium of the game. Numerical results for two-Genco system by both proposed method and conventional one are used to demonstrate that 1) the proposed framework can be successfully applied in analyzing the strategic behaviors of each Genco in changed markets and 2) both methods show considerably different results in terms of market stability or system reliability. The result indicates that generator maintenance scheduling strategy is one of the crucial strategic decision-makings whereby Gencos can maximize their profits in a competitive market environment.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.3
• /
• pp.509-516
• /
• 2011

This paper studies a novel method to find the profit-maximizing Nash Equilibriums in allocating generation quantities with consideration of ramp-rates under competitive market environment. Each GenCo in a market participates in a game to maximize its profit through competitions and play a game with bidding strategies. In order to find the Nash equilibriums it is necessary to search the feasible combinations of GenCos' strategies which satisfy every participant's profit and no one wants various constraints. During the procedure to find Nash equilibriums, the payoff matrix can be simplified as eliminating the dominated strategies. in each time interval. Because of the ramp-rate, generator's physically or technically limits to increase or decrease outputs in its range, it can restrict the number of bidding strategies of each generator at the next stage. So in this paper, we found the Nash Equilibriums for multi-stage generation allocation game considering the ramp-rate limits of generators. In the case studies, we analyzed the generation allocation game for a 12-hour multi-stage and compared it with the results of dynamic economic dispatch. Both of the two cases were considered generator's ramp-rate effects.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.17 no.2
• /
• pp.248-257
• /
• 2014

As observed in the recent war, suppression of enemy air defense operation is one of the major tactics, simultaneously conducted with high payoff target. Specifically, our air defense operation should be properly constructed, since the operating environment of our forces mostly consists with mountainous terrain, which makes detections of the enemy difficult. The effective arrangements of low altitude air defense radars can be suggested as a way of improving the detection capability of our forces. In this paper, we consider the location problem of low altitude air defense radar, and formulate it as an Integer Programming. Specifically, we surveyed the previous researches on facility location problems and applied two particularly relevant models(MCLP, MEXCLP) to our problem. The terrain factor was represented as demand points in the models. We verified the optimal radar locations for operational situations through simulation model which depicts simple battle field. In the simulation model, the performance of optimal radar locations are measured by the enemy detection rate. With a series of experiments, we may conclude that when locating low altitude air defense radars, it is important to consider the detection probability of radar. We expect that this finding may be helpful to make a more effective air defense plan.

• Journal of Korea Multimedia Society
• /
• v.12 no.12
• /
• pp.1819-1826
• /
• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.1
• /
• pp.1-8
• /
• 2019

In this paper, we design a new mobile crowdsourcing control scheme based on the incentive mechanism. By using a novel incentive mechanism, mobile nodes can get the maximum payoff when they report their true private information. As mobile nodes participate in the overlapping coalition formation game, they can effectively invest their resource while getting the higher reward. Simulation results clearly indicate that the proposed scheme has a better performance than the other existing schemes under various mobile crowdsourcing environments.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.17-26
• /
• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.