Towards a Comprehensive Framework for Cyber-Incident Response Decision Support in Smart Grids

TL;DR

A comprehensive framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity is presented, which enables grid operators to prioritize critical security measures.

Abstract

The modernization of power grid infrastructures necessitates the incorporation of decision support systems to effectively mitigate cybersecurity threats. This paper presents a comprehensive framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity. By analyzing risk attributes and optimizing defense strategies, this framework enables grid operators to prioritize critical security measures. Additionally, this paper incorporates findings on decision-making processes in intelligent power systems to present a comprehensive approach to grid cybersecurity. The proposed model aims to optimize the effectiveness and efficiency of grid cybersecurity efforts while offering insights into future grid management challenges.

Figures (6)

• Figure 1: dss Framework for Smart Grid Incident Response. This diagram outlines the key processes involved in the dss framework for incident response in smart grids. The framework consists of five main steps: organizational context, threat modeling, risk assessment, risk control, and risk response strategies. Additionally, the attack and defense risk attribute refinement process involves system adtree modeling, risk assessment over adtree, risk sensitivity analysis, risk-based optimization of defenses, and continuous monitoring. This holistic approach aims to enhance the cybersecurity posture of smart grids by identifying, evaluating, and mitigating potential risks effectively.
• Figure 2: Visualization of a Havex-based attack, following the classical Lockheed Martin kill-chain. The simulation concludes in 10 steps, with each step corresponding to a single kill-chain stage. The attack is based on two c2 nodes, the first being the master and the second the slave. As indicated in simulation steps 7, 8, and 9, communication attempts are initiated.
• Figure 3: Overview of the Multi-Criteria dss. This diagram illustrates the key components of the dss for incident response in smart grids. The system consists of three main components: Countermeasure to attack action matching, Multi-Criteria Decision Making, and Visualization of the adtree. This approach aims to provide grid operators with a decision-making tool to effectively respond to cybersecurity incidents.
• Figure 4: Attack Graph of the Havex Malware. Each node includes the name of the step, the attack action, and its kill-chain step. The edges include the mass function of the connection and the properties of the origin and target. The graph is used as input for the dss.
• Figure 5: Each graph shows the correlation between the weight of a criterion and the resulting score. The top graph represents the cost criterion, the middle graph the time to activate criterion, and the bottom graph the duration of activation criterion. The x-axis shows the value of the weight of the criterion, and the y-axis shows the sum of the scores of the countermeasures. The red line shows the trend line of the correlation between the weight of the criterion and the resulting score. On the x-axis, between 0.5 and 0.9, the sums remain constant; for better readability, we have truncated that part.