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 Active Defense Strategy Selection Method Based on Two-Way Signaling Game

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaohu Liu ◽  
Hengwei Zhang ◽  
Yuchen Zhang ◽  
Lulu Shao ◽  
Jihong Han
Keyword(s):  
Signal Transmission ◽  
Signaling Game ◽  
Strategy Selection ◽  
Network Attack ◽  
Defense Strategies ◽  
Defense Strategy ◽  
Active Defense ◽  
Bayesian Equilibrium ◽  
Security Research ◽  
Attack And Defense

Most network security research studies based on signaling games assume that either the attacker or the defender is the sender of the signal and the other party is the receiver of the signal. The attack and defense process is commonly modeled and analyzed from the perspective of one-way signal transmission. Aiming at the reality of two-way signal transmission in network attack and defense confrontation, we propose a method of active defense strategy selection based on a two-way signaling game. In this paper, a two-way signaling game model is constructed to analyze the network attack and defense processes. Based on the solution of a perfect Bayesian equilibrium, a defense strategy selection algorithm is presented. The feasibility and effectiveness of the method are verified using examples from real-world applications. In addition, the mechanism of the deception signal is analyzed, and conclusions for guiding the selection of active defense strategies are provided.

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 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.

scholarly journals Wireless Rechargeable Sensor Network Fault Modeling and Stability Analysis

2021 ◽  
Vol 3 (1) ◽  
pp. 47-54
Author(s):  
Mugunthan S. R.
Keyword(s):  
Optimal Strategies ◽  
Reproductive Value ◽  
Control Group ◽  
Defense Strategies ◽  
Security Issues ◽  
Proposed Model ◽  
The Stability ◽  
Wireless Rechargeable Sensor Network ◽  
Global And Local ◽  
Network Fault

Wide attention has been acquired by the field of wireless rechargeable sensor networks (WRSNs ) across the globe due to its rapid developments. Addressing the security issues in the WRSNs is a crucial task. The process of reinfection, charging and removal in WRSN is performed with a low-energy infected susceptible epidemic model presented in this paper. A basic reproductive value is attained after which the epidemic equilibrium and disease-free points of global and local stabilities are simulated and analyzed. Relationship between the reproductive value and rate of charging as well as the stability is a unique characteristic exhibited by the proposed model observed from the simulations. The WRSN and malware are built with ideal attack-defense strategies. When the reproductive value is not equal to one, the accumulated cost and non-optimal control group are compared in the sensor node evolution and the optimal strategies are validated and verified.

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.

Tracing the Ghosts in Our Ethical Shells

2020 ◽  
Author(s):  
Tam ngoc Nguyen
Keyword(s):  
Language Processing ◽  
Behavioral Science ◽  
Model Development ◽  
Continuous Model ◽  
Research Approach ◽  
Information Warfare ◽  
Scientific Model ◽  
Defense Strategies ◽  
Ethical Behaviors ◽  
Attack And Defense

We proposes a new scientific model that enables the ability to collect evidence, and explain the motivations behind people's cyber malicious/ethical behaviors. Existing models mainly focus on detecting already-committed actions and associated response strategies, which is not proactive. That is the reason why little has been done in order to prevent malicious behaviors early, despite the fact that issues like insider threats cost corporations billions of dollars annually, and its time to detection often lasts for more than a year.We address those problems by our main contributions of:+ A better model for ethical/malicious behavioral analysis with a strong focus on understanding people's motivations. + Research results regarding ethical behaviors of more than 200 participants, during the historic Covid-19 pandemic. + Novel attack and defense strategies based on validated model and survey results. + Strategies for continuous model development and integration, utilizing latest technologies such as natural language processing, and machine learning. We employed mixed-mode research approach of: integrating and combining proven behavioral science models, case studying of hackers, survey research, quantitative analysis, and qualitative analysis. For practical deployments, corporations may utilize our model in: improving HR processes and research, prioritizing plans based on the model's informed human behavioral metrics, better analysis in existing or potential cyber insider threat cases, generating more defense tactics in information warfare and so on.

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.

scholarly journals An Improved Approach for Reduction of Defect Density Using Optimal Module Sizes

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dinesh Verma ◽  
Shishir Kumar
Keyword(s):  
Goodness Of Fit ◽  
Defect Density ◽  
Optimal Size ◽  
Data Sets ◽  
Density Parameter ◽  
Proposed Model ◽  
Module Size ◽  
Curve Fitting Method ◽  
Improved Model ◽  
Effective Distribution

Nowadays, software developers are facing challenges in minimizing the number of defects during the software development. Using defect density parameter, developers can identify the possibilities of improvements in the product. Since the total number of defects depends on module size, so there is need to calculate the optimal size of the module to minimize the defect density. In this paper, an improved model has been formulated that indicates the relationship between defect density and variable size of modules. This relationship could be used for optimization of overall defect density using an effective distribution of modules sizes. Three available data sets related to concern aspect have been examined with the proposed model by taking the distinct values of variables and parameter by putting some constraint on parameters. Curve fitting method has been used to obtain the size of module with minimum defect density. Goodness of fit measures has been performed to validate the proposed model for data sets. The defect density can be optimized by effective distribution of size of modules. The larger modules can be broken into smaller modules and smaller modules can be merged to minimize the overall defect density.

Anatomy of SIP registration removal attack and defense strategies

2014 ◽  
Author(s):  
Ismail Melih Tas ◽  
Onur Ozbirecikli ◽  
Ugur Cagai ◽  
Erhan Taskin ◽  
Huseyin Tas
Keyword(s):  
Defense Strategies ◽  
Attack And Defense

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.

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