On Countering Ransomware Attacks Using Strategic Deception

Abstract

Ransomware attacks continue to be a major concern for critical systems that are vital for society e.g., healthcare, finance, and transportation. Traditional cyber defense mechanisms fail to pose dynamic measures to stop ransomware attacks from progressing through various stages in the attack process. To this end, intelligent cyber deception strategies can be effective when they leverage information about attacker strategies and deploy deceptive assets to increase the cost or complexity of a successful exploit or discourage continued attacker efforts. In this paper, we present a novel game theoretic approach that uses deception-based defense strategies at each of the ransomware attack stages for optimization of the decision-making to outsmart attacker advances. Specifically, we propose a multistage ransomware game model that deploys a combination of deception assets i.e., honeytokens, honeypots, honeyfiles, and network honeypots in subgames. Using closed-form backward induction, we evaluated Subgame-Perfect Nash Equilibrium (SPNE). We perform a numerical analysis using real-world data and statistics pertaining to the impact of ransomware attacks in the healthcare sector. Our healthcare case study evaluation results show that the use of deception technologies is favorable to the defender. This work elucidates the profound implications of strategic deception in cybersecurity, demonstrating its capacity to complicate successful exploits and consequently bolster the defense of key societal infrastructures.

Department(s)

Computer Science

Comments

National Science Foundation, Grant CNS-2243619

Keywords and Phrases

attacker/defender game; cyber deception; game theory; ransomware

International Standard Book Number (ISBN)

978-303174834-9

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Springer, All rights reserved.

Publication Date

01 Jan 2025

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