BAGS: A Tool to Quantify Smart Grid Resilience

Abstract

Abstract-In this paper, we present the Bayesian Attack Graph for Smart Grid (BAGS) tool to quantify smart grid resilience in the presence of multiple cyber-physical attacks. BAGS takes system functions, network architecture, applications and a vulnerability report as input and generates three Bayesian Networks at three different levels of hierarchy. The top level network is called Functional Bayesian Network that defines how smart grid functions are connected. System engineers can select a particular function on a dashboard and view the Network Bayesian Network of that function at the second level. They can also choose a particular network component to see the list of vulnerabilities and the probability of associated compromise at the third level. System engineers can incorporate this functionality into their system and analyze the impact of any compromised component of the smart grid system on its resilience. Furthermore, BAGS helps to identify the failure paths in advance from one power grid function to another so that they can devise secure strategies and deploy resources effectively and efficiently.

A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks

“…implement the Bayesian algorithm; instead, we use a Bayes.jar file to compute the probabilities of the functions and represent the network graph (see Figure 8). For details, refer to [13]. Figure 9 represents the unconditional probabilities of all functions of the SG test network.…”

“…(1) the function-based methodology to evaluate SGR [9][10][11][12], (2) Bayesian Attack Graph for Smart Grid (BAGS) to compute the likelihood of the compromise of cyber components of SG [13], (3) the risk analysis methodology, which combines the results of the function-based methodology and BAGS to compute risk for each cyber component and (4) efficient resource allocation using Reinforcement learning on the SG Bayesian graph (BAGS) [14] to compute optimal policies about whether to perform vulnerability assessment or patch a cyber component of SG whose vulnerability has already been discovered. The results and analysis of these approaches help power engineers develop more resilient power systems and improve situational awareness and the response of the system to ongoing CPA.…”

Fmdtools

“…For example, integrated circuits have well-defined properties that have led to a number of specialized fault simulation algorithms, which have since been implemented in software (May & Stechele, 2012;Niermann, Cheng, & Patel, 1992). Infrastructure often has specific hazards to assess, which has resulted in tools to consider natural disasters for cities (Fraser et al, 2016;McKenna, 2011) and cyber-physical threats in smart grids (Wadhawan & Neuman, 2017). Finally, assessing the hazards of autonomous vehicles in a real system is both costly and hazardous (Gambi, Müller, & Fraser, 2019), which has led to the development of a number of simulators that enable one to try different policies to approaching haz- ards which the vehicle will encounter (Jha, Banerjee, Cyriac, Kalbarczyk, & Iyer, 2018;Jha et al, 2019).…”