scholarly journals Network Attack and Defense Modeling and System Security Analysis: A Novel Approach Using Stochastic Evolutionary Game Petri Net

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zenan Wu ◽  
Liqin Tian ◽  
Yi Zhang ◽  
Yan Wang ◽  
Yuquan Du
Keyword(s):  
Petri Net ◽  
Evolutionary Game ◽  
Security Analysis ◽  
System Security ◽  
Analysis Method ◽  
Network Attack ◽  
Network Connection ◽  
Novel Approach ◽  
Average System ◽  
Attack And Defense

At present, most network security analysis theory assumes that the players are completely rational. However, this is not consistent with the actual situation. In this paper, based on the effectiveness constraints on both sides with network attack and defense, with the help of stochastic Petri net and evolutionary game theory, the Petri net model of network attack and defense stochastic evolutionary game is reconstructed, the specific definition of the model is given, and the modeling method is given through the network connection relationship and attack and defense strategy set. Using this model, a quantitative analysis of network attack events is carried out to solve a series of indicators related to system security, namely, attack success rate, average attack time, and average system repair time. Finally, the proposed model and analysis method are applied to a classic network attack and defense process for experimental analysis, and the results verify the rationality and accuracy of the model and analysis method.

Generation Method of Power Network Security Defense Strategy Based on Markov Decision Process

2021 ◽  
Author(s):  
Wang Yang ◽  
Liu Dong ◽  
Wang Dong ◽  
Xu Chun
Keyword(s):  
Network Security ◽  
Response Time ◽  
Markov Decision Process ◽  
Decision Process ◽  
Evolutionary Game ◽  
Power Network ◽  
Network Attack ◽  
Defense Strategy ◽  
Markov Decision ◽  
Attack And Defense

Aiming at the problem that the current generation method of power network security defense strategy ignores the dependency relationship between nodes, resulting in closed-loop attack graph, which makes the defense strategy not generate attack path, resulting in poor defense effect and long generation response time of power network security defense strategy, a generation method of power network security defense strategy based on Markov decision process is proposed. Based on the generation of network attack and defense diagram, the paper describes the state change of attack network by using Markov decision-making process correlation principle, introduces discount factor, calculates the income value of attack and defense game process, constructs the evolutionary game model of attack and defense, solves the objective function according to the dynamic programming theory, obtains the optimal strategy set and outputs the final results, and generates the power network security defense strategy. The experimental results show that the proposed method has good defense effect and can effectively shorten the generation response time of power network security defense strategy.

scholarly journals Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3014
Author(s):  
Pengxi Yang ◽  
Fei Gao ◽  
Hua Zhang
Keyword(s):  
Evolutionary Game ◽  
Stable State ◽  
Payoff Matrix ◽  
Game Model ◽  
Network Attack ◽  
Evolutionary Stable Strategies ◽  
Player Game ◽  
Attack And Defense ◽  
Mathematical Derivation

We formalize the adversarial process between defender and attackers as a game and study the non-cooperative evolutionary game mechanism under bounded rationality. We analyze the long-term dynamic process between the attacking and defending parties using the evolutionary stable strategies derived from the evolutionary game model. First, we construct a multi-player evolutionary game model consisting of a defender and multiple attackers, formally describe the strategies, and construct a three-player game payoff matrix. Then, we propose two punishment schemes, i.e., static and dynamic ones. Moreover, through the combination of mathematical derivation with simulation, we obtain the evolutionary stable strategies of each player. Different from previous work, in this paper, we consider the influence of strategies among different attackers. The simulation shows that (1) in the static punishment scheme, increasing the penalty can quickly control the occurrence of network attacks in the short term; (2) in the dynamic punishment scheme, the game can be stabilized effectively, and the stable state and equilibrium values are not affected by the change of the initial values.

scholarly journals Study on Stochastic Differential Game Model in Network Attack and Defense

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaotong Xu ◽  
Gaocai Wang ◽  
Jintian Hu ◽  
Yuting Lu
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Evolutionary Game ◽  
Markov Property ◽  
Game Model ◽  
Strategy Selection ◽  
Network Attack ◽  
Disturbance Factor ◽  
Attack And Defense ◽  
Selection Of

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.

scholarly journals Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yanhua Liu ◽  
Hui Chen ◽  
Hao Zhang ◽  
Ximeng Liu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Dynamic Network ◽  
Game Model ◽  
Strategy Selection ◽  
Network Attack ◽  
Learning Mechanism ◽  
Defense Strategy ◽  
Attack And Defense

Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM.

scholarly journals MAIAD: A Multistage Asymmetric Information Attack and Defense Model Based on Evolutionary Game Theory

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 215 ◽  
Author(s):  
Yu Yang ◽  
Bichen Che ◽  
Yang Zeng ◽  
Yang Cheng ◽  
Chenyang Li
Keyword(s):  
Game Theory ◽  
Asymmetric Information ◽  
Evolutionary Game Theory ◽  
Rapid Development ◽  
Evolutionary Game ◽  
Defense Strategy ◽  
Security Issues ◽  
Defense Model ◽  
Attack And Defense ◽  
Systems Simulation

With the rapid development and widespread applications of Internet of Things (IoT) systems, the corresponding security issues are getting more and more serious. This paper proposes a multistage asymmetric information attack and defense model (MAIAD) for IoT systems. Under the premise of asymmetric information, MAIAD extends the single-stage game model with dynamic and evolutionary game theory. By quantifying the benefits for both the attack and defense, MAIAD can determine the optimal defense strategy for IoT systems. Simulation results show that the model can select the optimal security defense strategy for various IoT systems.

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