Lazy Defenders Are Almost Optimal against Diligent Attackers
Authors: Avrim Blum, Nika Haghtalab, Ariel Procaccia
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analytically demonstrate that in zero-sum security games, lazy defenders, who simply keep optimizing against perfectly informed attackers, are almost optimal against diligent attackers, who go to the effort of gathering a reasonable number of observations. This result implies that, in some realistic situations, limited surveillance may not need to be explicitly addressed. But rather than designing new or improved algorithms, as is common in the security games literature, we take the opposite approach by analytically demonstrating that, in realistic situations, limited surveillance may not be a major concern. |
| Researcher Affiliation | Academia | Avrim Blum Computer Science Department Carnegie Mellon University avrim@cs.cmu.edu; Nika Haghtalab Computer Science Department Carnegie Mellon University nika@cmu.edu; Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not mention providing open-source code for its methodology. |
| Open Datasets | No | The paper is theoretical and presents mathematical models and proofs rather than using empirical datasets for training. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |