Research map / Learning theory and algorithms / Submodular and game algorithms
When Can the Defender Effectively Deceive Attackers in Security Games?
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use deceptive coverage against an attacker who believes the observed coverage | Defender in SeGDC · Generated SSGs | defender utility | Helped |
| Use deceptive coverage against a maximin attacker | Defender in SeGDC · Generated SSGs | defender utility | Helped |
| Use deceptive coverage against an equilibrium-reasoning attacker | Defender in SeGDC · Generated SSGs | defender utility | Hurt |
| Use equilibrium reasoning to respond to defender deception rather than the standard SSE setting | Attacker in SeGDC · Generated SSGs | attacker utility | No clear effect |
| Increase the deception interval from αi = βi = 0.05 to αi = βi = 0.15 | Defender in SeGDC against ignorant or maximin attacks · Generated SSGs | defender utility | Helped |
| Use a maximin response instead of an ignorant response to deception | Attacker in SeGDC · Generated SSGs | attacker utility | Helped |
| Use defender deception rather than no deception when the attacker uses a maximin response | Defender with k < L · General SeGDC | defender utility | Hurt |
| Use defender deception rather than no deception when the attacker uses a maximin response | Defender with k ≥ L and no resource-allocation constraint · General SeGDC | defender utility | No clear effect |
| Use equilibrium deception, which removes the defender’s first-mover advantage | Defender in SeGDC · General SeGDC | defender utility | Hurt |
Closest papers by meaning
- Deception in Finitely Repeated Security Games (AAAI 2019 · similarity 0.91)
- Behavioral Learning in Security Games: Threat of Multi-Step Manipulative Attacks (AAAI 2023 · similarity 0.89)
- Manipulating a Learning Defender and Ways to Counteract (NeurIPS 2019 · similarity 0.89)
- Optimal Attack and Defense for Reinforcement Learning (AAAI 2024 · similarity 0.85)
- GUARD: Constructing Realistic Two-Player Matrix and Security Games for Benchmarking Game-Theoretic Algorithms (NeurIPS 2025 · similarity 0.83)
- Regret Minimization in Stackelberg Games with Side Information (NeurIPS 2024 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.