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

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yan Mi ◽  
Hengwei Zhang ◽  
Hao Hu ◽  
Jinglei Tan ◽  
Jindong Wang
Keyword(s):  
Network Security ◽  
Real Time ◽  
Differential Game ◽  
Random Effects ◽  
Game Model ◽  
Random Disturbance ◽  
Stochastic Differential Game ◽  
Defense Strategy ◽  
Random Disturbances ◽  
Attack And Defense

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.

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 Stochastic Differential Game-Based Malware Propagation in Edge Computing-Based IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Miao ◽  
Shuai Li
Keyword(s):  
Differential Game ◽  
Infinite Horizon ◽  
Optimal Strategies ◽  
Edge Computing ◽  
Game Model ◽  
Stochastic Differential Game ◽  
Equilibrium Solutions ◽  
Stochastic Fluctuations ◽  
Malware Propagation ◽  
Nash Equilibrium Solutions

Internet of Things (IoT) has played an important role in our daily life since its emergence. The applications of IoT cover from the traditional devices to intelligent equipment. With the great potential of IoT, there comes various kinds of security problems. In this paper, we study the malware propagation under the dynamic interaction between the attackers and defenders in edge computing-based IoT and propose an infinite-horizon stochastic differential game model to discuss the optimal strategies for the attackers and defenders. Considering the effect of stochastic fluctuations in the edge network on the malware propagation, we construct the Itô stochastic differential equations to describe the propagation of the malware in edge computing-based IoT. Subsequently, we analyze the feedback Nash equilibrium solutions for our proposed game model, which can be considered as the optimal strategies for the defenders and attackers. Finally, numerical simulations show the effectiveness of our proposed game model.

scholarly journals Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaohu Liu ◽  
Hengwei Zhang ◽  
Yuchen Zhang ◽  
Lulu Shao
Keyword(s):  
Transition Probability ◽  
Evolutionary Game ◽  
Evolutionary Process ◽  
Learning Object ◽  
Game Model ◽  
Strategy Selection ◽  
Learning Mechanism ◽  
Defense Strategy ◽  
Network Game ◽  
Attack And Defense

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.

scholarly journals Attack-Defense Differential Game Model for Network Defense Strategy Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 50618-50629 ◽  
Author(s):  
Hengwei Zhang ◽  
Lv Jiang ◽  
Shirui Huang ◽  
Jindong Wang ◽  
Yuchen Zhang
Keyword(s):  
Differential Game ◽  
Game Model ◽  
Strategy Selection ◽  
Defense Strategy ◽  
Network Defense

scholarly journals A Novel Attack-and-Defense Signaling Game for Optimal Deceptive Defense Strategy Choice

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yongjin Hu ◽  
Han Zhang ◽  
Yuanbo Guo ◽  
Tao Li ◽  
Jun Ma
Keyword(s):  
Traditional Approach ◽  
Optimal Strategies ◽  
Signaling Game ◽  
Game Model ◽  
Defense Strategies ◽  
Defense Strategy ◽  
Bayesian Equilibrium ◽  
Proposed Model ◽  
Improved Model ◽  
Attack And Defense

Increasingly, more administrators (defenders) are using defense strategies with deception such as honeypots to improve the IoT network security in response to attacks. Using game theory, the signaling game is leveraged to describe the confrontation between attacks and defenses. However, the traditional approach focuses only on the defender; the analysis from the attacker side is ignored. Moreover, insufficient analysis has been conducted on the optimal defense strategy with deception when the model is established with the signaling game. In our work, the signaling game model is extended to a novel two-way signaling game model to describe the game from the perspectives of both the defender and the attacker. First, the improved model is formally defined, and an algorithm is proposed for identifying the refined Bayesian equilibrium. Then, according to the calculated benefits, optimal strategies choice for both the attacker and the defender in the game are analyzed. Last, a simulation is conducted to evaluate the performance of the proposed model and to demonstrate that the defense strategy with deception is optimal for the defender.

scholarly journals Study on cybersecurity attack-defense visualization method based on intelligent connected vehicle

2021 ◽  
Vol 268 ◽  
pp. 01010
Author(s):  
Yafei Wang ◽  
Shengqiang Han ◽  
Nan Zhang
Keyword(s):  
Real Time ◽  
System Architecture ◽  
Visual Display ◽  
Experimental Results ◽  
Game Model ◽  
Connected Vehicle ◽  
Visualization Method ◽  
Logical Relationship ◽  
Experimental Environment ◽  
Attack And Defense

Attack test and defense verification are important ways to effectively evaluate the cybersecurity performance of Intelligent Connected Vehicle (ICV). This paper investigates the problem of attack-defense visualization in ICV cybersecurity. For the purpose of promoting cybersecurity research capabilities, a novel Cybersecurity Attack-Defense Visualization method based on Intelligent Connected Vehicle (CADV-ICV) is proposed. In this scheme, an Attack-Defense Game model (ADG) is designed so that the logical relationship between the attack and defense can be studied through a system architecture. Then, the CADV-ICV method is implemented through three layers that are hardware layer, software layer and visualization layer. Finally, through an Intelligent Connected Vehicle, two TV monitors, a computer and a server, a real experimental environment is built to test the CADV-ICV method. The experimental results show that CADV-ICV can realize the visual display of attack-defense process, attack messages, defense state, real-time message monitoring, and attack-defense principle for 10 car’s components.

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