With the increasing penetration of cyber systems in the power grid, it is becoming increasingly imperative to deploy adequate security measures all across the grid to secure it against any kind of cyber threat. Since financial resources for investment in security are limited, optimal allocation of these cybersecurity resources in the grid is extremely important. At the same time, optimization of these investments proves to be challenging due to the uncertain behavior of attackers and the dynamically changing threat landscape. Existing solutions for this problem either do not address the dynamic behavior of adversaries or lack in the practical feasibility of the defense models. This paper addresses the problem of optimizing investment strategies in the cybersecurity infrastructure of a smart grid using a game-theoretic approach. The attacker is modeled using various attacker profiles which represent the possible types of adversaries in the context of CPS. Each profile has certain characteristics to bring out the aspect of uncertain behavior of the adversaries. The defender is modeled with various pragmatic characteristics that can be easily translated to the real-world grid scenarios for implementation. These characteristics include the standards laid down by the North American Electric Reliability Corporation (NERC) for Critical Infrastructure Protection (CIP) commonly known as the NERC-CIP standards. The game-theoretic framework allows us to obtain optimal strategies that the defender of the grid can adopt to minimize its losses against the possible attack threats on the grid. The concept is illustrated by a simplistic 3-bus power system model case study which depicts how the solution can be translated to practical implementation in the actual grid.
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A Novel Methodology for Cybersecurity Investment Optimization in Smart Grids using Attack-Defense Trees and Game Theory
Securing cyber-physical systems (CPS) like the Smart Grid against cyber attacks is making it imperative for the system defenders to plan for investing in the cybersecurity resources of cyber-physical critical infrastructure. Given the constraint of limited resources that can be invested in the cyber layer of the cyber-physical smart grid, optimal allocation of these resources has become a priority for the defenders of the grid. This paper proposes a methodology for optimizing the allocation of resources for the cybersecurity infrastructure in a smart grid using attack-defense trees and game theory. The proposed
methodology uses attack-defense trees (ADTs) for analyzing the cyber-attack paths (attacker strategies) within the grid and possible defense strategies to prevent those attacks. The attack-defense strategy space (ADSS) provides a comprehensive list of interactions between the attacker and the defender of the grid. The proposed methodology uses the ADSS from the ADT analysis for a game-theoretic formulation (GTF) of attacker-defender interaction. The GTF allows us to obtain strategies for the defender in order to optimize cybersecurity resource allocation in the smart grid. The implementation of the proposed methodology is validated using a synthetic smart grid model equipped with
cyber and physical components depicting the feasibility of the methodology for real-world implementation.
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- Award ID(s):
- 1739969
- NSF-PAR ID:
- 10311922
- Date Published:
- Journal Name:
- IEEE Innovative Smart Grid Technologies (ISGT)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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