Partial Adversarial Behavior Deception in Security Games
Authors: Thanh H. Nguyen, Arunesh Sinha, He He
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception. and 6 Experiments We analyze the impact of the attacker deception on: (i) the deceptive attacker s utility benefit; (ii) the defender s utility loss; and (iii) the defender s learning outcome. |
| Researcher Affiliation | Academia | Thanh H. Nguyen1 , Arunesh Sinha2 , He He1 1University of Oregon 2Singapore Management University |
| Pseudocode | No | The paper describes algorithms (GOSAQ, GAMBO) but does not provide them in a structured pseudocode block or clearly labeled algorithm figure. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We use the game generator, GAMUT (http://gamut.stanford/edu) to generate player payoffs. In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ. The paper does not provide a specific link, DOI, repository name, or formal citation for a publicly available or open dataset that was used. GAMUT is a generator, not a dataset itself. |
| Dataset Splits | No | The paper describes creating a 'training dataset' (In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ.), but it does not specify any training/validation/test dataset splits needed for reproducibility (e.g., percentages, absolute counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments, such as CPU/GPU models, memory, or cloud instances. |
| Software Dependencies | No | The paper mentions using 'GAMUT' as a game generator but does not specify its version number or any other software libraries, frameworks, or solvers with their respective version numbers. |
| Experiment Setup | Yes | In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ. We plot the experiment results in three cases (the xaxis in the plotted figures): (i) varying the λ of the boundedly rational attacker; (ii) varying the percentage of deceptive attacks ( f f+1); and (iii) varying resource-target ratio ( K T ). |