scholarly journals Evolutionary Game for Confidentiality in IoT-enabled Smart Grids

2020 ◽  
Author(s):  
Svetlana Boudko ◽  
Peder Aursand ◽  
Habtamu Abie
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Smart Grids ◽  
A Priori ◽  
Evolutionary Game ◽  
Real Life ◽  
Advanced Metering Infrastructure ◽  
A Priori Analysis ◽  
Advanced Metering ◽  
Security Game

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

scholarly journals Evolutionary Game for Confidentiality in IoT-Enabled Smart Grids

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 582
Author(s):  
Svetlana Boudko ◽  
Peder Aursand ◽  
Habtamu Abie
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Smart Grids ◽  
A Priori ◽  
Evolutionary Game ◽  
Real Life ◽  
Advanced Metering Infrastructure ◽  
Advanced Metering ◽  
Security Game ◽  
Attacks And Defenses

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks and defenses in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modeled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work was to explore the space of possible behaviors of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defense.

scholarly journals A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Mode Choice ◽  
Evolutionary Game ◽  
Payoff Matrix ◽  
Game Model ◽  
Stable States ◽  
Taxi Service ◽  
Service Mode ◽  
The Impact

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

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