Bayesian Game Model Research Articles

Defense strategy selection based on incomplete information game for the false data injection attack

With the rapid development and wide application of sensing devices and communication networks, the traditional power system has transformed into a cyber physical power system (CPPS) gradually. The constant interaction of information flow and power flow exposes the grid to potential cyber-attack risks. In this paper, the defense strategy selection approaches are proposed based on incomplete information game against false data injection attack (FDIA) in different scenarios. Firstly, the attack-defense tree model containing economic indicators and a measure of attack is established, and the expected revenues of both attacker and defender are calculated in detail. Secondly, the static Bayesian attack-defense game model in which the defender grasps incomplete information is constructed, considering two cases of low-tech attacker and high-tech attacker. Then, the zero-sum attack-defense game model in which the attacker masters incomplete information is structured, considering the changing attack space. Finally, the simulations are carried out on the IEEE 30-bus system, and the optimal defense strategies in the case of two incomplete information are obtained by solving the equilibrium point. The simulation results demonstrate that the proposed method is effective in cyber security defense system, and can provide guiding ideology and theoretical basis for operators to deploy defense measures.

PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

Enhancing ecological uncertainty predictions in pollution control games through dynamic Bayesian updating

This study presents a dynamic Bayesian game model designed to improve predictions of ecological uncertainties leading to natural disasters. It incorporates historical signal data on ecological indicators. Participants, acting as decision-makers, receive signals about an unknown parameter-observations of a random variable’s realization values before a specific time, offering insights into ecological uncertainties. The essence of the model lies in its dynamic Bayesian updating, where beliefs about unknown parameters are refined with each new signal, enhancing predictive accuracy. The main focus of our paper is to theoretically validate this approach, by presenting a number of theorems that prove its precision and efficiency in improving uncertainty estimations. Simulation results validate the model’s effectiveness in various scenarios, highlighting its role in refining natural disaster forecasts.

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive Systems

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modelled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrates the applicability and potency in mitigating security risks.

A multi-energy pricing strategy for port integrated energy system based on Bayesian Stackelberg game

This paper proposes a method for multi-energy pricing strategy by constructing Bayesian Stackelberg model, in order to increase the social welfare and extend the optimal results to the case where private information about energy users (EUs) is unknown. Specifically, in Bayesian Stackelberg game model, the port acts as a leader and EUs are competitive followers. Furthermore, the decision processes of port and EUs are coupled together by the probability of consuming electricity and heat. In addition, a price regulation mechanism is designed, which is composed of fictitious play (FP) algorithm and gradient descent algorithm. Consequently, the balance of energy supply and demand can be obtained by loop iteration of these two algorithms. Numerical results have demonstrated that the multi-energy pricing strategy can achieve the balance between supply and demand and improve the social welfare.

Incentive-compatibility auction and renewable energy pricing strategy based on incomplete Information game of price cost declaration

ABSTRACT The construction and development of the Electricity Spot Market (ESM) are beneficial to the fair distribution of power resources and the improvement of power system regulation ability. The original ESM was not designed to include renewable energy. Since there is almost no marginal cost for renewable energy, the renewable energy can not copy the thermal power unit price model after participating in ESM. The prevalent Locational Marginal Price (LMP) mechanism within the extant ESM structure encounters limitations wherein optimal benefits accrue to buyers and sellers only under specific conditions. This paper thus introduces an innovative electricity pricing model and trading settlement strategy tailored to the characteristics of wind turbine-generated power. To begin, we devise a novel electricity pricing model grounded in the construction expenses and predictive precision intrinsic to renewable energy sources. Subsequently, in synergy with the ESM framework, we formulate a Bayesian game model to compute benefits contingent upon bid success probabilities. A pricing strategy based on Vickrey – Clarke – Groves (VCG) is selected, which pays the other generating strategy as an incremental cost by removing itself. Empirical evaluations affirm the efficacy and rationality of the proposed approach. The outcomes of our study not only validate its applicability but also lay the groundwork for a fresh theoretical paradigm underpinning energy trading modalities in future ESM contexts.

Trading strategy for virtual power plant clusters based on a multi-subject game model

With the rapid development of renewable energy and the continuous growth of new loads, VPP has become an important form of smart grid and energy internet due to its flexible and effective management of distributed energy. During the operation of Virtual power plant, there is a game relationship between the system operator and VPP, and they are in a non-complete information environment. However, most of the current game optimization modeling is under the condition of complete information, and the game model based on complete information cannot solve this problem. This article focuses on the VPP cluster trading problem based on non-complete information game theory, constructs a Bayesian game model for multiple VPPs with multiple subjects under the master-slave game framework by introducing the Bayesian concept to optimize the cluster transactions within VPPs, and verifies the effectiveness of the model through simulation experiments. The experimental results show that the multi-VPP multi-subject Bayesian game model established in the study can guarantee the privacy of each subject and effectively reduce PAR, thus ensuring the security and stability of the VPP network and reducing cost expenditures, which has practicality in actual VPP cluster transactions.

Research on Price Discrimination Behavior Governance of E-Commerce Platforms—A Bayesian Game Model Based on the Right to Data Portability

The behavior of e-commerce platforms using big data algorithms to implement “big data killing” is not only rejected by regular customers, but also creates many difficulties for supervision by relevant government departments. In order to study how to curb the price discrimination behavior of e-commerce platforms in the case of taking regular customers as the leaders, this paper introduces the right to data portability and considers two types of regular customers with high price sensitivity and low price sensitivity. Then, we build a Bayesian game model between e-commerce platforms and regular customers on the basis of the established general game model. Our experiments show that when the reuse value coefficient of personal data is high, the e-commerce platform will choose a uniform pricing strategy, which indicates that the right to data portability can curb the price discrimination behavior of the e-commerce platform to a certain extent. Moreover, when the proportion of high-sensitivity consumers among regular customers increases, e-commerce platforms will be prompted to choose the uniform pricing strategy, which indicates that consumers can curb price discrimination behaviour on e-commerce platforms by increasing their sensitivity for price change.

Security risk assessment of projects in high-risk areas based on attack-defense game model

Assessing the security risk of projects in high-risk areas is particularly important. This paper develops a security risk assessment model for projects in high-risk areas based on the target loss probability model and Bayesian game model. This model is modeled from the perspective of attack-defense confrontation and addresses the issue that traditional risk assessment focuses on the analysis of the attacker yet neglects to analyze the defender—the defender’s optimum defensive information is not quantitatively determined. The risk level, optimum defensive resource value, and optimum defensive strategy of the project are determined through the analysis of a project in the high-risk area. This enables the project’s risk manager to adjust the defensive resources reasonably and optimally, confirming the objectivity and feasibility of the model and offering a new benchmark for security risk assessment, which has significant practical implications.

V2V Energy Trading in Residential Microgrids Considering Multiple Constraints via Bayesian Game

The V2V (vehicle-to-vehicle) energy swapping strategy provides an alternative charging for electric vehicles (EVs) to alleviate the problem of overload caused by the un-coordinately charges in residential microgrids (MGs) during the peak. However, stochastic factors of EVs and neglected network losses cause inflexibility and impracticality of the existing methods. Focused on the stochastic factors of energy trading caused by EVs and network constraints in residential MGs scenario, a V2V Energy Trading in Residential Microgrids (VETRM) considering multiple constraints via Bayesian game model is proposed. Frist, the Bayesian game modeled types of players by information including the stochastic characteristics of EVs, which results in uncertainties that the game participants determine the roles of seller or buyer that depends on the states of power surplus or lack. Then, utility functions based on the sensitivity analysis to the impact of V2V transactions on the network and to guarantee the exchange of energies to inviolate network constraints are established. The solution of model based on game equilibrium has been rigorously derived. Two scenarios fulfill the comparisons of existing models and proposed model for simulation including IEEE 33 and IEEE 123 bus system that the proposed model improve the revenues of EV in reducing energy loss and smoothing the peak of the power system to demonstrate the effectiveness of the VETRM model.

Towards a Secure Fog-Computing Cyber Space: A Bayesian Game-Theoretic Risk Management Framework

Cyber security is one of the most difficult and fast-growing concerns today's enterprises are focusing on. The practice of reducing potentially damaging and unknown events that pose a danger to cyber security is known as cyber security risk management. The Game Theoretic Approach is a popular cyber security risk or threat management strategy (GTA). This study provides a paradigm for cybersecurity risk or threat handling based on a game-theoretic approach to Fog computing, which will encourage proactive cyber risk management and improve cyber-operational efficiency/effectiveness. The method is written in such a way that the PyQt4 framework acts as a shield for the Fog server, performing inline packet inspection and logging any malicious packets to the console and a database on the server using Snort. The study proposes a Bayesian game model for risk management in the cyber domain.

Playing Bayesian Stackelberg game model for optimizing the vulnerability level of security incident system in petrochemical plants

Constrained by the limited security resources of petrochemical plants, optimizing the vulnerability level of the security incident system plays a significant role in preventing and controlling security incidents. Previous research for security vulnerability has concentrated on the protection system, neglecting the influence of interconnections among vulnerability elements. The intelligence and struggle features of Security Incident System Vulnerability (SISV) elements are critical for optimizing the vulnerability level and characterizing the evolution of intelligence of security incident participants. By studying the game characteristics among SISV elements, this paper proposes the Bayesian Stackelberg SISV game model based on the security incident protection model established. In the game model, the strategies for protective vulnerability and aggressive vulnerability are defined as setting the security layer sub-units and the protective vulnerability levels, and setting the intrusion routes and the aggressive vulnerability levels, respectively. The model optimizes the security system vulnerability levels within the different security layer sub-units, based on the available security resources. Despite being demonstrated via an illustrative case, the defender's return on investment is significantly higher by using the SISV game model, the methodology can easily be tailored to a wide variety of petrochemical plants.

Attack-Defense Game Model with Multi-Type Attackers Considering Information Dilemma.

Today, people rely heavily on infrastructure networks. Attacks on infrastructure networks can lead to significant property damage and production stagnation. The game theory provides a suitable theoretical framework for solving the problem of infrastructure protection. Existing models consider only the beneficial effects that the defender obtains from information gaps. If the attacker's countermeasures are ignored, the defender will become passive. Herein, we consider that a proficient attacker with a probability in the game can fill information gaps in the network. First, we introduce the link-hiding rule and the information dilemma. Second, based on the Bayesian static game model, we establish an attack-defense game model with multiple types of attackers. In the game model, we consider resource-consistent and different types of distributions of the attacker. Then, we introduce the solution method of our model by combining the Harsanyi transformation and the bi-matrix game. Finally, we conduct experiments using a scale-free network. The result shows that the defender can be benefited by hiding some links when facing a normal attacker or by estimating the distribution of the attacker correctly. The defender will experience a loss if it ignores the proficient attacker or misestimates the distribution.

Exploiting Non-Cooperative Game Against Cache Pollution Attack in Vehicular Content Centric Network

Vehicular Content Centric Network (VCCN) has been proposed to address the issues of user mobility and sporadic connectivity in Vehicular Ad hoc Network (VANET). The in-network caching of VCCN can indeed improve the performance of content distribution in terms of cache hit and delivery latency by making each node store frequently accessed data. However, data caching unfortunately suffers from Cache Pollution Attack (CPA) that sends out fabricated requests to pollute the data cache. To prevent the degradation of network performance caused by such an attack, we propose a detection and defense scheme for CPA by adopting game theory. We model the attack scenario into a non-cooperative game model. First, we prove the non-existence of pure strategy Nash Equilibrium (NE) in the attacker and defender game, and propose a normal form game model with complete information and an extensive Bayesian form game model with incomplete information. Under the guidance of NE, we propose a punishment strategy to prevent the conspiracy attack and make the attackers behave normally. Moreover, we propose a cooperative detection scheme to give the Road Side Unit (RSU) the final decision on CPA based on the direct and indirect suspicious lists which are generated by each node according to the observed traffic pattern. Simulation evaluations demonstrate that our scheme outperforms state-of-the-art schemes in terms of cache hit, detecting ratio, and cache accuracy for detecting and defending CPA.

Bayesian game model based unsupervised sentiment analysis of product reviews

Sentiment Analysis is a task of computationally recognizing and contextualizing opinions stated in a text. We mainly assess whether the writer's attitude towards a specific topic, or a product, is positive, negative, or neutral. Numerous machine learning and fuzzy logic methods have been reconnoitered for sentiment analysis. Yet, the application of mathematical optimization techniques for sentiment tagging is still unexplored. This study presents a novel mathematical framework for sentiment analysis of reviews based on Game Theory. We identify whether the sentiment of a review is positive or negative. In the first step, we comprehend a review and derive context scores from review comments using the SentiWordNet lexicon. We comprehensively combine the computed context and rating scores using the Bayesian Game Model to deduce the sentiment of reviews. Experimental results on three benchmark review datasets, viz. Food, Mobile, and Electronics demonstrate that the proposed model yields state-of-the-art results. We also statistically validated the stability and correctness of the results. The proposed model ensures rational and consistent results. The utility of the game theory model for sentiment analysis creates a new paradigm for diverse NLP tasks.

The Method of Trajectory Selection Based on Bayesian Game Model

To cope with the problem that most of the en-route spatial-temporal resource allocation in the collaborative trajectory options program (CTOP) only considers the air traffic control system command center (ATCSCC) while ignoring the needs of the airlines, which results in the loss of fairness, this study explores resource allocation methods oriented to airline trajectory preferences with optional trajectory and entry slots of flights over the flow constrained area (FCA) as the research object. Using game theory to analyze airline trajectory preference information and a Bayesian game model based on mixed strategies is constructed, the process of incomplete information game among airlines is studied. The equilibrium theory is used to solve the guarantee strategy of airline trajectory selection, which makes the airline trajectory selection strategy robust and provides a basis for the selection of schemes for ATCSCC to implement en-route network resource allocation under the CTOP. Experimental analysis was carried out to verify the feasibility of the method based on the actual operation data of high-altitude sectors of Shanghai. The results show that the solution obtained by the game can provide airlines with flight trajectory and entry slots over the FCA that are more in line with their actual operational needs and which provide data reference for the ATCSCC to select the final plan in multiple global Pareto optimal solutions in the subsequent process of the CTOP so as to better play the decision-making role of airlines in the CTOP while improving the fairness of en-route resource allocation.

Research on Game Theory of Air Traffic Management Cyber Physical System Security

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if the two parties have different goals. The offensive and defensive game process of the air traffic management cyber physical system is a non-cooperative and incomplete information dynamic game. The attacker can choose to camouflage the type of attack launched. The attack detection device configured in the system has a certain probability that the attack type can be successfully detected. According to the type of attack detected, the defender updates the posterior belief of the attack type and selects the corresponding protective strategies. According to the game process of offense and defense, a dynamic Bayesian game model of the air traffic management cyber physical system is established, the possible perfect Bayesian Nash equilibrium and its existence conditions are solved, and a complete mathematical model is constructed. The analysis shows that the dynamic Bayesian game model of the air traffic management cyber physical system can help the system defender to quickly obtain an equilibrium strategy and reduce the loss of the system as much as possible.

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

In modern world of sensing and distributive systems, traditional Wireless Sensor Networks (WSN) has to deal with new challenges, such as multiple application requirements, dynamic and heterogeneous networks. Senor nodes in WSN are resource constrained in terms of energy, communication range, bandwidth, processing delay and memory. Numerous solutions are proposed to optimize the performance and to increase the lifetime of WSN by introducing new resource management principles. Effective and intelligent resource management in WSN involves in resource identification, resource scheduling, and resource utilization. This paper proposes a Bayesian Game Model (BGM) approach to efficiently identify the best node with the maximum resource in WSN for data transmission, considering energy, bandwidth, and computational delay. The scheme operates as follows: (1) Sensor nodes information such as residual energy, available bandwidth, and node ID, etc., is gathered (2) Energy and bandwidth of each node are used to generate the payoff matrix (3) Implementation of node identification scheme is based on payoff matrix, utilities assigned, strategies and reputation of each node (4) Find Bayesian Nash Equilibrium condition using Starring algorithm (5) Solving the Bayesian Nash Equilibrium using Law of Total Probability and identifying the best node with maximum resources (6) Adding/Subtracting reward (reputation factor) to winner/looser node. Simulation results show that the performance of the proposed Bayesian game model approach for resource identification in WSN is better as compared with the Efficient Neighbour Discovery Scheme for Mobile WSN (ENDWSN). The results indicate that the proposed scheme has up to 12% more resource identification accuracy rate, 10% increase in the average number of efficient resources discovered and 8% less computational delay as compared to ENDWSN.

The role of bilinguals in the Bayesian naming game

We study the recently introduced Bayesian naming game model, in which the one-shot learning of the minimal naming game is replaced by a more realistic learning process defined according to Bayesian inference. The results are compared with those obtained from the minimal naming game model. We focus on the dynamics of the bilingual population, providing analytical estimates of the upper bound for the number of bilinguals in both models based on the mean-field equations, and validate them through numerical simulations of the multi-agent models. We show that in the Bayesian model the maximum number of bilinguals is always lower with respect to the minimal naming game and that the two models are characterized by qualitatively different time evolutions.