Security analysis of online digital goods business based on stochastic game net model

Liu, Jun-Jie; Zhao, Chunxiao

doi:10.1002/sec.1381

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Through the work of Wang and Vrieze et al . [16, 18, 20, 21], an NE of a discounted stochastic game can be found by solving a non‐linear programming problem as follows:

\underset{u^{1}, u^{2}, s^{1}, s^{2}}{truemin} falsefalse{1^{T} false[u^{k} - R^{k} false(p^{1}, p^{2} false) - d P false(p^{1}, p^{2} false) u^{k} false] falsefalse}, k = 1, 2 \ddot{x} s . t . R^{1} false(p_{i} false) π^{2} false(p_{i} false) + δ T false(p_{i}, u^{1} false) π^{2} false(p_{i} false) \leq u^{1} false(p_{i} false) 1, i = 1, . . ., (||, P)

π^{1} false(p_{i} false)^{T} R^{2} false(p_{i} false) + δ π^{1} false(p_{i} false)^{T} T false(p_{i}, u^{2} false) \leq u^{2} false(p_{i} false) 1^{T}, i = 1, \dots, (||, P)

where

T false(p, u^{k} false) = false[false[p false(p_{1} falsefalse| p, t^{1}, t^{2} false), . . ., p false(p_{(||, P)} falsefalse| p, t^{1}, t^{2} false) false]^{T} u^{k} false]_{t^{1} \in T^{1}, t^{2} \in T^{2}}, p...

…”

Section: Virtualisation Risk Assessment For Enterprise Cloud Servicmentioning

confidence: 99%

Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Liu

Rong

2018

IET Information Security

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“…Through the work of Wang and Vrieze et al . [16, 18, 20, 21], an NE of a discounted stochastic game can be found by solving a non‐linear programming problem as follows:

\underset{u^{1}, u^{2}, s^{1}, s^{2}}{truemin} falsefalse{1^{T} false[u^{k} - R^{k} false(p^{1}, p^{2} false) - d P false(p^{1}, p^{2} false) u^{k} false] falsefalse}, k = 1, 2 \ddot{x} s . t . R^{1} false(p_{i} false) π^{2} false(p_{i} false) + δ T false(p_{i}, u^{1} false) π^{2} false(p_{i} false) \leq u^{1} false(p_{i} false) 1, i = 1, . . ., (||, P)

π^{1} false(p_{i} false)^{T} R^{2} false(p_{i} false) + δ π^{1} false(p_{i} false)^{T} T false(p_{i}, u^{2} false) \leq u^{2} false(p_{i} false) 1^{T}, i = 1, \dots, (||, P)

where

T false(p, u^{k} false) = false[false[p false(p_{1} falsefalse| p, t^{1}, t^{2} false), . . ., p false(p_{(||, P)} falsefalse| p, t^{1}, t^{2} false) false]^{T} u^{k} false]_{t^{1} \in T^{1}, t^{2} \in T^{2}}, p...

…”

Section: Virtualisation Risk Assessment For Enterprise Cloud Servicmentioning

confidence: 99%

Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Liu

Rong

2018

IET Information Security

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“…Wang et al considered an enterprise network for modeling and analyzing purpose by using attack–defense stochastic game Petri nets. Lv and Zhao used SGN to model and analyze digital goods business. The proposed model provides a logical environment for defining unpredictability in digital goods business with proposed indicators.…”

Section: Related Workmentioning

confidence: 99%

Security in IoT network based on stochastic game net model

Kaur

Sood

2017

Int J Network Mgmt

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Summary The growing prevalence of Internet of Things (IoT) ushers itself with various security concerns. Being complex in nature, traditional security countermeasures cannot be applied directly to IoT networks. Addressing this problem, this paper aims to combine the capabilities of 2 traditional methods namely, game theory (GT) and stochastic Petri nets (SPN), such that the resultant model is compatible for complex IoT networks. Game theory does not have enough modeling capability to cope up with complexity of IoT networks. However, it has an advantage of providing a priori idea of attacker's actions and strategies with the help of Nash equilibrium. This information is used by administrators to devise appropriate action plan to detect and prevent attacks on network. On the other hand, SPN is a dynamic, scalable and probabilistic model, which overcomes the limitations of GT. Nevertheless, it is not able to compute best strategies (Nash equilibrium) of attacker. Therefore, this paper proposes stochastic game net (SGN)–based model for security in IoT, which combines the advantages of SPN and GT. The novelty of the work lies in the fact that this is the first attempt to define SGN for handling security issues in IoT. Simulations performed using OPNET tool show that SGN shows 5.94% and 5.91% improvement in terms of confidentiality, 6.4% and 8% improvement in terms of integrity, and 6.7% and 8.9% improvement in terms of availability over SPN and GT, respectively.

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“…Static defense strategies based on specific rules and attack signature matching can no longer effectively cope with frequent and multiple attacks on the network. Network security is dynamic rather than static, and therefore the defense strategy should evolve dynamically with the advancement of the attack and defense process, and the return should be maximized for the given constraints of people, time, the changing situation, resources, capabilities, and preferences [1]. Network security is relative rather than absolute, and a key factor in an effective defense measure is to minimize the expected security loss for different attack scenarios and defense strategies.…”

Section: Introductionmentioning

confidence: 99%

Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

Liu¹,

Zhang²,

Zhang³

et al. 2020

Security and Communication Networks

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The basic hypothesis of evolutionary game theory is that the players in the game possess limited rationality. The interactive behavior of players can be described by a learning mechanism that has theoretical advantages in modeling the network security problem in a real society. The current network security evolutionary game model generally adopts a replicator dynamic learning mechanism and assumes that the interaction between players in the group conforms to the characteristics of uniform mixed distribution. However, in an actual network attack and defense scenario, the players in the game have limited learning capability and can only interact with others within a limited range. To address this, we improved the learning mechanism based on the network topology, established the learning object set based on the learning range of the players, used the Fermi function to calculate the transition probability to the learning object strategy, and employed random noise to describe the degree of irrational influence in the learning process. On this basis, we built an attack and defense evolutionary network game model, analyzed the evolutionary process of attack and defense strategy, solved the evolution equilibrium, and designed a defense strategy selection algorithm. The effectiveness of the model and method is verified by conducting simulation experiments for the transition probability of the players and the evolutionary process of the defense group strategy.

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Security analysis of online digital goods business based on stochastic game net model

Cited by 4 publications

References 16 publications

Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Security in IoT network based on stochastic game net model

Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

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