A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries
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Abstract (arXiv)
Cyber deception is one of the key approaches used to mislead attackers by hiding or providing inaccurate system information. There are two main factors limiting the real-world application of existing cyber deception approaches. The first limitation is that the number of systems in a network is assumed to be fixed. However, in the real world, the number of systems may be dynamically changed. The second limitation is that attackers' strategies are simplified in the literature. However, in the real world, attackers may be more powerful than theory suggests. To overcome these two limitations, we propose a novel differentially private game theoretic approach to cyber deception. In this proposed approach, a defender adopts differential privacy mechanisms to strategically change the number of systems and obfuscate the configurations of systems, while an attacker adopts a Bayesian inference approach to infer the real configurations of systems. By using the differential privacy technique, the proposed approach can 1) reduce the impacts on network security resulting from changes in the number of systems and 2) resist attacks regardless of attackers' reasoning power. The experimental results demonstrate the effectiveness of the proposed approach.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2022One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacycited by 26
- 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
- 2021Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDScited by 3
- 2024Cyber Deception: State of the art, Trends and Open challengescited by 0
- 2024Cyber Deception: State of the art, Trends and Open challenges11 Sep 2024
- 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacy31 Dec 2022
- 2022One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy13 Mar 2022
- 2021Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS23 Nov 2021