Network Security Situation Assessment Approach Based on Attack-Defense Stochastic Game Model

Weng, Fangyu; Zhang, Ru; Guo, Yi

doi:10.1007/978-3-030-00012-7_15

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Defense Stochastic Game Model (ADSGM). 33 The ADSGM framework evaluates the cyber threat of a network node being attacked and considers the potential impact of threat propagation caused by node relevance. The authors recognized the computer user as a third player in the environment.…”

Section: Liu Et Al Established a Game Theory Framework Titled Attack-mentioning

confidence: 99%

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Impact of computer users on cyber defense strategies

Colvett,

Petty,

Bland

2023

Systems Engineering

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Cybersecurity research often focuses primarily or exclusively on the interactions between the attacker, trying to exploit the computer system, and the defender, trying to protect it. However, including the computer users is important because the users’ requirements are the reason the computer system exists.An extension of the Petri net formalism, Petri Nets with Players, Strategies, and Costs (PNPSC) was used to model cyberattacks described in the MITRE Common Attack Pattern Enumeration and Classification database. PNPSC models include the attacker, defender, and computer user as “players” attempting to achieve competing goals. Each player can observe the current marking of a subset of the PNPSC net's places and change the stochastic firing rates of a subset of the net's transitions in order to achieve their goals. A mapping between the markings of a player's observable places and the desired firing rates of player's controllable transitions is the player's strategy.A reinforcement learning algorithm was integrated with PNPSC models of three cyberattack patterns to learn strategies for the defender in simulations both with and without a representation of the computer user. A simulation experiment showed that the defender's reward was lower and the defender's learned strategy was different when the user was represented. A second simulation experiment and statistical analysis confirmed that the differences were not due simply to randomness. With the user represented, the system defender must balance security against usability. This research provides a more complete cyberattack model and shows that user models are important in future cybersecurity simulation.

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Section: Liu Et Al Established a Game Theory Framework Titled Attack-mentioning

confidence: 99%

“…Liu et al. established a game theory framework titled Attack‐Defense Stochastic Game Model (ADSGM) 33 . The ADSGM framework evaluates the cyber threat of a network node being attacked and considers the potential impact of threat propagation caused by node relevance.…”

Section: Related Workmentioning

confidence: 99%

Impact of computer users on cyber defense strategies

Colvett,

Petty,

Bland

2023

Systems Engineering

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“…It can search the required strategies in stochastic evolutionary games and find the optimal results of the network attack and defense. Liu et al [21] integrated multiple network security elements (such as assets, threats, and vulnerabilities) of multisensor mobile phones into standard data sets to improve the awareness of network security.…”

Section: Related Workmentioning

confidence: 99%

“…erefore, researchers tried to further improve the effectiveness of the model and more accurately describe the evolutionary game of attack and defense by using stochasticity [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In the stochastic game of this paper, the attackers will try to interfere or destroy the network environment.…”

Section: Introductionmentioning

confidence: 99%

Study on Stochastic Differential Game Model in Network Attack and Defense

Wang

et al. 2020

Security and Communication Networks

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In recent years, evolutionary game theory has been gradually applied to analyze and predict network attack and defense for maintaining cybersecurity. The traditional deterministic game model cannot accurately describe the process of actual network attack and defense due to changing in the set of attack-defense strategies and external factors (such as the operating environment of the system). In this paper, we construct a stochastic evolutionary game model by the stochastic differential equation with Markov property. The evolutionary equilibrium solution of the model is found and the stability of the model is proved according to the knowledge of the stochastic differential equation. And we apply the explicit Euler numerical method to analyze the evolution of the strategy selection of the players for different problem situations. The simulation results show that the stochastic evolutionary game model proposed in this paper can get a steady state and obtain the optimal defense strategy under the action of the stochastic disturbance factor. In addition, compared with other kinds of literature, we can conclude that the return on security investment of this model is better, and the strategy selection of the attackers and defenders in our model is more suitable for actual network attack and defense.

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“…Considering the randomness of network system states and attack-defense strategies, and their effects on the states of the game system, Orojloo and Azgomi [24] proposed to assess network security using a stochastic game model with the two phases of intrusion and interruption. Liu et al [25] analyzed the impact of threat propagation on the security of a network system, evaluated system states through a stochastic game model, and provided enhanced defense strategies based on the Nash equilibrium. Kandoussi et al [26] combined stochastic game theory with attack graphs to model attack-defense interactions, analyzed potential attack paths, and deployed corresponding security measures.…”

Section: Introductionmentioning

confidence: 99%

Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

Mao

Zhang

et al. 2021

Security and Communication Networks

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In a real-world network confrontation process, attack and defense actions change rapidly and continuously. The network environment is complex and dynamically random. Therefore, attack and defense strategies are inevitably subject to random disturbances during their execution, and the transition of the network security state is affected accordingly. In this paper, we construct a network security state transition model by referring to the epidemic evolution process, use Gaussian noise to describe random effects during the strategy execution, and introduce a random disturbance intensity factor to describe the degree of random effects. On this basis, we establish an attack-defense stochastic differential game model, propose a saddle point equilibrium solution method, and provide an algorithm to select the optimal defense strategy. Our method achieves real-time defense decision-making in network attack-defense scenarios with random disturbances and has better real-time performance and practicality than current methods. Results of a simulation experiment show that our model and algorithm are effective and feasible.

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Network Security Situation Assessment Approach Based on Attack-Defense Stochastic Game Model

Cited by 4 publications

References 10 publications

Impact of computer users on cyber defense strategies

Impact of computer users on cyber defense strategies

Study on Stochastic Differential Game Model in Network Attack and Defense

Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

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