Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Non-Additive Security Games
Authors: Sinong Wang, Fang Liu, Ness Shroff
AAAI 2017 | Venue PDF | 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 EMAIL Ness Shroff Departments of ECE and CSE, The Ohio State University Columbus, OH 43210, USA EMAIL |
| 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. |