Playing Repeated Stochastic Security Games Against Non-Stationary Attackers

This paper investigates a repeated stochastic security game against a non-stationary attacker. Most of the work to date assumes that the defender has a repeated interaction with a fixed type of attacker. In fact, the defender is more likely to encounter changing attackers in multi-round games. A defender faces an attacker whose identity is unknown. The attacker type changes stochastically over time and the defender cannot detect when these changes occur. We adopt the BPR (Bayesian Policy Reuse) algorithm to detect the switches of the attacker, and the defender could play the accurate policy correspondingly. The experiment results show that BPR algorithm could accurately detect switches and help the defender gain more utilities than the EXP3-S algorithm.

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Publication Details

Journal
Mathematics
Published
2025-08-22
DOI
https://doi.org/10.3390/math13172697
Citations
1
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.49

Funders

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article

Playing Repeated Stochastic Security Games Against Non-Stationary Attackers

Ling Chen, Runfa Zhang
1 citations
Mathematics
Network Security and Intrusion Detection
1.49
article

Playing Repeated Stochastic Security Games Against Non-Stationary Attackers

Ling Chen, Runfa Zhang
article en
1 citations

Abstract

This paper investigates a repeated stochastic security game against a non-stationary attacker. Most of the work to date assumes that the defender has a repeated interaction with a fixed type of attacker. In fact, the defender is more likely to encounter changing attackers in multi-round games. A defender faces an attacker whose identity is unknown. The attacker type changes stochastically over time and the defender cannot detect when these changes occur. We adopt the BPR (Bayesian Policy Reuse) algorithm to detect the switches of the attacker, and the defender could play the accurate policy correspondingly. The experiment results show that BPR algorithm could accurately detect switches and help the defender gain more utilities than the EXP3-S algorithm.

MathematicsVol. 13(17)
Taiyuan Normal University (CN), Shanxi University (CN), Hubei University (CN)
National Natural Science Foundation of China, Shanxi University, Hubei University
Openalex Percentile: Top 14%
Network Security and Intrusion Detection
1.49
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Playing Repeated Stochastic Security Games Against Non-Stationary Attackers — Ling Chen, Runfa Zhang · Mathematics (2025) | TGRS Research Map | TGRS