Non-Additive Security Games
Authors: Sinong Wang, Fang Liu, Ness Shroff
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our theoretical framework to the network security game. We characterize settings under which we find a polynomial time algorithm for computing optimal strategies. In other settings we prove the problem is NP-hard and provide an approximation algorithm. In Fig. 3, we examine the distributions of the benefit function and its common utility function in the following two kinds of network: Erd os-Renyi network G(n, p) and scalefree network G(n, α)... The more comprehensive numerical results can be found in the supplementary material. |
| Researcher Affiliation | Academia | Sinong Wang, Fang Liu Department of ECE, The Ohio State University Columbus, OH 43210, USA {wang.7691, liu.3977}@osu.edu Ness Shroff Departments of ECE and CSE, The Ohio State University Columbus, OH 43210, USA shroff.11@osu.edu |
| Pseudocode | Yes | Algorithm 1 Vertex Mapping from Vertex to Pure Strategy; Algorithm 2 Separable Approximation |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions network types like 'Erd os-Renyi network G(n, p)', 'scale-free network G(n, α)', and '39 nodes Italian communication network', but does not provide specific access information (e.g., URL, DOI, or a specific citation to a public repository) for any dataset instance used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |