Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC ha...Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC has become a transformative paradigm for addressing these challenges,particularly in intrusion detection and anomaly mitigation.The widespread connectivity of IoT edge networks has exposed them to various security threats,necessitating robust strategies to detect malicious activities.This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory(BGT)and double deep Q-learning(DDQL).The proposed framework integrates BGT to model attacker and defender interactions for dynamic threat level adaptation and resource availability.It also models a strategic layout between attackers and defenders that takes into account uncertainty.DDQL is incorporated to optimize decision-making and aids in learning optimal defense policies at the edge,thereby ensuring policy and decision optimization.Federated learning(FL)enables decentralized and unshared anomaly detection for sensitive data between devices.Data collection has been performed from various sensors in a real-time EC-IoT network to identify irregularities that occurred due to different attacks.The results reveal that the proposed model achieves high detection accuracy of up to 98%while maintaining low resource consumption.This study demonstrates the synergy between game theory and FL to strengthen anomaly detection in EC-IoT networks.展开更多
The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-...The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-out-of-2 rational secret sharing based on Bayesian game and considering different classes of protocol parties, we propose a 2-out-of-2 secret sharing scheme to solve cooperative problem of a rational secret sharing scheme being played only once. Moreover, we prove that the strategy is a perfect Bayesian equilibrium, adopted only by the parties in their decision-making according to their belief system （denoted by the probability distribution） and Bayes rule, without requiring simultaneous channels.展开更多
To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomple...To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.展开更多
In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attac...In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.展开更多
In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) ...In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.展开更多
IoT security is very crucial to IoT applications,and security situational awareness can assess the overall security status of the IoT.Traditional situational awareness methods only consider the unilateral impact of at...IoT security is very crucial to IoT applications,and security situational awareness can assess the overall security status of the IoT.Traditional situational awareness methods only consider the unilateral impact of attack or defense,but lackconsideration of joint actions by both parties.Applying gametheory to security situational awareness can measure the impact of the opposition and interdependence of the offensive and defensive parties.This paper proposes an IoT security situational awareness method based on Q-Learning and Bayesian game.Through Q-Learning update,the long-term benefits of action strategies in specific states were obtained,and static Bayesian game methods were used to solve the Bayesian Nash Equilibrium of participants of different types.The proposed method comprehensively considers offensive and defensive actions,obtains optimal defense decisions in multi-state and multi-type situations,and evaluates security situation.Experimental results prove the effectiveness of this method.展开更多
Wireless ad ho network is becoming a new research fronter, in which security is an important issue. Usually some nodes act maliciously and they are able to do different kinds of Denial of Service (Dos). Because of the...Wireless ad ho network is becoming a new research fronter, in which security is an important issue. Usually some nodes act maliciously and they are able to do different kinds of Denial of Service (Dos). Because of the limited resource, intrusion detection system (IDS) runs all the time to detect intrusion of the attacker which is a costly overhead. In our model, we use game theory to model the interactions between the intrusion detection system and the attacker, and a realistic model is given by using Bayesian game. We solve the game by finding the Bayesian Nash equilibrium. The results of our analysis show that the IDS could work intermittently without compromising on its effectiveness. At the end of this paper, we provide an experiment to verify the rationality and effectiveness of the proposed model.展开更多
Residential flexible resource is attracting much attention in demand response(DR)for peak load shifting.This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communiti...Residential flexible resource is attracting much attention in demand response(DR)for peak load shifting.This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information.Communities in the scenario can decide whether to participate in DR in each stage,but the decision is the private information that is unknown to other communities.To optimize the energy consumption,a Bayesian game approach is formulated,in which the probability characteristic of the decision-making of residential communities is described with Markov chain considering human behavior of bounded rationality.Simulation results show that the proposed approach can benefit all residential communities and power grid,but the optimization effect is slightly inferior to that in complete information game approach.展开更多
The present study discusses the relationships between two independently developed models of games with incomplete information, hypergames （Bennett, 1977） and Bayesian games （Harsanyi, 1967）. The authors first show...The present study discusses the relationships between two independently developed models of games with incomplete information, hypergames （Bennett, 1977） and Bayesian games （Harsanyi, 1967）. The authors first show that any hypergame can naturally be reformulated in terms of Bayesian games in an unified way. The transformation procedure is called Bayesian representation of hypergame. The authors then prove that some equilibrium concepts defined for hypergames are in a sense equivalent to those for Bayesian games. Furthermore, the authors discuss carefully based on the proposed analysis how each model should be used according to the analyzer＇s purpose.展开更多
This paper discusses the relationship of two independently developed models of games with incomplete information,hierarchical hypergames and Bayesian games.It can be considered as a generalization of the previous stud...This paper discusses the relationship of two independently developed models of games with incomplete information,hierarchical hypergames and Bayesian games.It can be considered as a generalization of the previous study on the theoretical comparison of simple hypergames and Bayesian games(Sasaki and Kijima,2012) by taking into account hierarchy of perceptions,i.e.,an agent's perception about the other agents' perceptions,and so on.The authors first introduce the general way of transformation of any hierarchical hypergames into corresponding Bayesian games,which was called as the Bayesian representation of hierarchical hypergames.The authors then show that some equilibrium concepts for hierarchical hypergames can be associated with those for Bayesian games and discuss implications of the results.展开更多
To explore the influence of quantum information on the common social problem of honesty and trickery,we propose a Bayesian model for the quantum prisoners’dilemma game.In this model,the players’strategy formation is...To explore the influence of quantum information on the common social problem of honesty and trickery,we propose a Bayesian model for the quantum prisoners’dilemma game.In this model,the players’strategy formation is regarded as a negotiation of their move contract based on their types of decision policies,honesty or trickery.Although the implementation of quantum information cannot eliminate tricky players,players in our model can always end up with higher payoffs than in the classical game.For a good proportion of a credibility parameter value,a rational player will take an honest action,which is in remarkable contrast to the observation that players tend to defect in the classical prisoners’dilemma game.This research suggests that honesty will be promoted to enhance cooperation with the assistance of quantum information resources.展开更多
In the context of highway merging scenarios where ramp vehicles encounter rear vehicles on the main lane,a significant challenge arises due to the competition for the right of way,exacerbated by the stochastic nature ...In the context of highway merging scenarios where ramp vehicles encounter rear vehicles on the main lane,a significant challenge arises due to the competition for the right of way,exacerbated by the stochastic nature of driving styles.This situation can lead to traffic congestion and even collisions if not managed effectively.To address these issues,this paper presents an optimal cooperative merging strategy based on Bayesian Nash Equilibrium for connected and automated vehicles.The approach begins by analyzing the inherent randomness in driving styles exhibited at on-ramps.Specifically,a Principal Component Analysis method is applied to extract key features with lower dimensions.These features are then used to estimate the probability distributions of driving styles for both ramp and mainline vehicles.Subsequently,a cooperative merging model is developed,taking into account the obtained probability distributions of driving styles.This model leverages the Markov Bayesian Game Decision Process framework to represent the decision-making interactions between mainline and ramp vehicles.Furthermore,a Deep Reinforcement Learning Framework integrated with Bayesian Game is proposed,to learn and derive the optimal cooperative merging strategy under stochastic driving styles.Simulation results indicate that the proposed model can make feasible and reasonable decisions at on-ramps,effectively avoiding collision accidents caused by stochastic driving styles.展开更多
In modern low-carbon industrial parks,various distributed renewable energy resources are employed to fulfill production needs.Despite the growing capacity of renewable energy generation,a significant portion of the po...In modern low-carbon industrial parks,various distributed renewable energy resources are employed to fulfill production needs.Despite the growing capacity of renewable energy generation,a significant portion of the power produced by these renewable resources remains unconsumed,resulting in a waste of resources.Within an industrial park,microgrids that both generate and consume energy resources act as energy prosumers.Peer-topeer(P2P)trading provides an efficient means of utilizing renewable energy among these energy prosumers,who possess both power generation and consumption capabilities.However,within the current market mechanism,each prosumer retains private information that is not disclosed on the network.To address the issue of incomplete information among multiple prosumers during the decision-making process,we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers.The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads.Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics,which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method.These decisions are aimed at mini-mizing each prosumer’s electricity costs.The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.展开更多
Based on the basis of the two stage dynamic game of complete information and purely tactful perfect equilibrium theory, the non cooperative gaming between the police department and the criminals is analyzed. Dyn...Based on the basis of the two stage dynamic game of complete information and purely tactful perfect equilibrium theory, the non cooperative gaming between the police department and the criminals is analyzed. Dynamic game can be proved to forecast and explain potential tactful choices of the police department and the criminals at various stages, so as to analyze the essence of the law enforcement by the theoretical models.展开更多
基金The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Group Project under grant number(RGP2/337/46)The research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/352-4.
文摘Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC has become a transformative paradigm for addressing these challenges,particularly in intrusion detection and anomaly mitigation.The widespread connectivity of IoT edge networks has exposed them to various security threats,necessitating robust strategies to detect malicious activities.This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory(BGT)and double deep Q-learning(DDQL).The proposed framework integrates BGT to model attacker and defender interactions for dynamic threat level adaptation and resource availability.It also models a strategic layout between attackers and defenders that takes into account uncertainty.DDQL is incorporated to optimize decision-making and aids in learning optimal defense policies at the edge,thereby ensuring policy and decision optimization.Federated learning(FL)enables decentralized and unshared anomaly detection for sensitive data between devices.Data collection has been performed from various sensors in a real-time EC-IoT network to identify irregularities that occurred due to different attacks.The results reveal that the proposed model achieves high detection accuracy of up to 98%while maintaining low resource consumption.This study demonstrates the synergy between game theory and FL to strengthen anomaly detection in EC-IoT networks.
基金Supported by the Major National Science and Technology program (2011ZX03005-002)the National Natural Science Foundation of China (60872041, 61072066, 60963023, 60970143)the Fundamental Research Funds for the Central Universities (JY10000903001, JY10000901034)
文摘The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-out-of-2 rational secret sharing based on Bayesian game and considering different classes of protocol parties, we propose a 2-out-of-2 secret sharing scheme to solve cooperative problem of a rational secret sharing scheme being played only once. Moreover, we prove that the strategy is a perfect Bayesian equilibrium, adopted only by the parties in their decision-making according to their belief system （denoted by the probability distribution） and Bayes rule, without requiring simultaneous channels.
基金supported by the National Natural Science Fundation of China (60974082 60874085)+2 种基金the Fundamental Research Funds for the Central Universities (K50510700004)the Technology Plan Projects of Guangdong Province (20110401)the Team Project of Hanshan Normal University (LT201001)
文摘To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.
基金This work was supported by the National Key Research and Development Program(No.2016YFB0901900)the National Natural Science Foundation of China(No.61733018)The authors would like to thank Prof.Peng Yi for his helpful suggestions.
文摘In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.
文摘In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.
基金the National Key Research and Development Program of China(No.2017YFB1400700).
文摘IoT security is very crucial to IoT applications,and security situational awareness can assess the overall security status of the IoT.Traditional situational awareness methods only consider the unilateral impact of attack or defense,but lackconsideration of joint actions by both parties.Applying gametheory to security situational awareness can measure the impact of the opposition and interdependence of the offensive and defensive parties.This paper proposes an IoT security situational awareness method based on Q-Learning and Bayesian game.Through Q-Learning update,the long-term benefits of action strategies in specific states were obtained,and static Bayesian game methods were used to solve the Bayesian Nash Equilibrium of participants of different types.The proposed method comprehensively considers offensive and defensive actions,obtains optimal defense decisions in multi-state and multi-type situations,and evaluates security situation.Experimental results prove the effectiveness of this method.
文摘Wireless ad ho network is becoming a new research fronter, in which security is an important issue. Usually some nodes act maliciously and they are able to do different kinds of Denial of Service (Dos). Because of the limited resource, intrusion detection system (IDS) runs all the time to detect intrusion of the attacker which is a costly overhead. In our model, we use game theory to model the interactions between the intrusion detection system and the attacker, and a realistic model is given by using Bayesian game. We solve the game by finding the Bayesian Nash equilibrium. The results of our analysis show that the IDS could work intermittently without compromising on its effectiveness. At the end of this paper, we provide an experiment to verify the rationality and effectiveness of the proposed model.
基金the Natural Science Research Project of Jiangsu Higher Education Institutions(No.20KJB470024).
文摘Residential flexible resource is attracting much attention in demand response(DR)for peak load shifting.This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information.Communities in the scenario can decide whether to participate in DR in each stage,but the decision is the private information that is unknown to other communities.To optimize the energy consumption,a Bayesian game approach is formulated,in which the probability characteristic of the decision-making of residential communities is described with Markov chain considering human behavior of bounded rationality.Simulation results show that the proposed approach can benefit all residential communities and power grid,but the optimization effect is slightly inferior to that in complete information game approach.
基金supported by Grant-in-Aid for Japan Society for the Promotion of Science(JSPS) Fellows, No.21-9482
文摘The present study discusses the relationships between two independently developed models of games with incomplete information, hypergames （Bennett, 1977） and Bayesian games （Harsanyi, 1967）. The authors first show that any hypergame can naturally be reformulated in terms of Bayesian games in an unified way. The transformation procedure is called Bayesian representation of hypergame. The authors then prove that some equilibrium concepts defined for hypergames are in a sense equivalent to those for Bayesian games. Furthermore, the authors discuss carefully based on the proposed analysis how each model should be used according to the analyzer＇s purpose.
文摘This paper discusses the relationship of two independently developed models of games with incomplete information,hierarchical hypergames and Bayesian games.It can be considered as a generalization of the previous study on the theoretical comparison of simple hypergames and Bayesian games(Sasaki and Kijima,2012) by taking into account hierarchy of perceptions,i.e.,an agent's perception about the other agents' perceptions,and so on.The authors first introduce the general way of transformation of any hierarchical hypergames into corresponding Bayesian games,which was called as the Bayesian representation of hierarchical hypergames.The authors then show that some equilibrium concepts for hierarchical hypergames can be associated with those for Bayesian games and discuss implications of the results.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61773399,61673389,and 61273202)the Special Funded Project of China Postdoctoral Science Foundation(Grant No.2017T100792).
文摘To explore the influence of quantum information on the common social problem of honesty and trickery,we propose a Bayesian model for the quantum prisoners’dilemma game.In this model,the players’strategy formation is regarded as a negotiation of their move contract based on their types of decision policies,honesty or trickery.Although the implementation of quantum information cannot eliminate tricky players,players in our model can always end up with higher payoffs than in the classical game.For a good proportion of a credibility parameter value,a rational player will take an honest action,which is in remarkable contrast to the observation that players tend to defect in the classical prisoners’dilemma game.This research suggests that honesty will be promoted to enhance cooperation with the assistance of quantum information resources.
基金supported in part by China Scholarship Council under Grant 202206830054the National Science Foundation of China under Grant 52072175+1 种基金Jiangsu Outstanding Youth Fund Project under Grant BK20220078by the Fundamental Research Funds for the Central Universities under Grant NI2023002.
文摘In the context of highway merging scenarios where ramp vehicles encounter rear vehicles on the main lane,a significant challenge arises due to the competition for the right of way,exacerbated by the stochastic nature of driving styles.This situation can lead to traffic congestion and even collisions if not managed effectively.To address these issues,this paper presents an optimal cooperative merging strategy based on Bayesian Nash Equilibrium for connected and automated vehicles.The approach begins by analyzing the inherent randomness in driving styles exhibited at on-ramps.Specifically,a Principal Component Analysis method is applied to extract key features with lower dimensions.These features are then used to estimate the probability distributions of driving styles for both ramp and mainline vehicles.Subsequently,a cooperative merging model is developed,taking into account the obtained probability distributions of driving styles.This model leverages the Markov Bayesian Game Decision Process framework to represent the decision-making interactions between mainline and ramp vehicles.Furthermore,a Deep Reinforcement Learning Framework integrated with Bayesian Game is proposed,to learn and derive the optimal cooperative merging strategy under stochastic driving styles.Simulation results indicate that the proposed model can make feasible and reasonable decisions at on-ramps,effectively avoiding collision accidents caused by stochastic driving styles.
基金supported by the project of Science and Technology Project of the State Grid Corporation of China(1400-202312333A-1-1-ZN).
文摘In modern low-carbon industrial parks,various distributed renewable energy resources are employed to fulfill production needs.Despite the growing capacity of renewable energy generation,a significant portion of the power produced by these renewable resources remains unconsumed,resulting in a waste of resources.Within an industrial park,microgrids that both generate and consume energy resources act as energy prosumers.Peer-topeer(P2P)trading provides an efficient means of utilizing renewable energy among these energy prosumers,who possess both power generation and consumption capabilities.However,within the current market mechanism,each prosumer retains private information that is not disclosed on the network.To address the issue of incomplete information among multiple prosumers during the decision-making process,we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers.The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads.Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics,which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method.These decisions are aimed at mini-mizing each prosumer’s electricity costs.The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.
文摘Based on the basis of the two stage dynamic game of complete information and purely tactful perfect equilibrium theory, the non cooperative gaming between the police department and the criminals is analyzed. Dynamic game can be proved to forecast and explain potential tactful choices of the police department and the criminals at various stages, so as to analyze the essence of the law enforcement by the theoretical models.
基金supported by Foundation for Innovative Research Groups of National Natural Science Foundation of China(NSFC)(61321002)National Science Fund for Distinguished Young Scholars(60925011)+2 种基金Projects of Major International(Regional)Joint Research Program NSFC(61120106010)Beijing Education Committee Cooperation Building Foundation Project,Program for Changjiang Scholars and Innovative Research Team in University(IRT1208)Chang Jiang Scholars Program and National Natural Science Foundation of China(61203078)
