Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods

Abstract

False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model.

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

“…Furthermore, due to limitations of space, we cannot conduct comparative study with more existing works within this paper. We may further do this in the future with, say, [47][48][49][50][51], which involves RL-based methods, visual language models, game-theoretic approaches, and evolutionary strategies. Additionally, it is necessary to further investigate the dynamics of swarm confrontation under conditions of incomplete information in order to enhance the broader applicability of the bio-inspired confrontation algorithm.…”

Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach

“…As shown in Figure 2, the TCN module consists of three main components: causal convolution, dilated convolution, and residual blocks [32]. In this module, the convolutional kernel is denoted as k = 3, and the dilation factor is denoted as d = [1,2,4], where the dilation factor is typically a factorial of 2.…”

“…Now, a typical CPS merges physical power transmission systems with computer networks. The optimization and standardization of this smart grid are of vital importance to ensure its safe and stable operation [1,2]. However, due to the large-scale integration of smart terminals and the transmission of data through networks by numerous smart devices, the continuous improvement of the intelligent level of the power grid also provides rich attack entrances for malicious attackers.…”