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].
Multi-Defender Strategic Filtering Against Spear-Phishing Attacks
Authors: Aron Laszka, Jian Lou, Yevgeniy Vorobeychik
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize both Stackelberg multi-defender equilibria, corresponding to short-term strategic dynamics, as well as Nash equilibria of the simultaneous game between all users and the attacker, modeling long-term dynamics, and exhibit a polynomial-time algorithm for computing short-term (Stackelberg) equilibria. We ο¬nd that while Stackelberg multi-defender equilibrium need not exist, Nash equilibrium always exists, and remarkably, both equilibria are unique and socially optimal. |
| Researcher Affiliation | Academia | Aron Laszka Electrical Engineering and Computer Sciences Dept. University of California, Berkeley Berkeley, CA Jian Lou and Yevgeniy Vorobeychik Institute for Software Integrated Systems Dept. of Electrical Engineering and Computer Science Vanderbilt University Nashville, TN |
| Pseudocode | Yes | Algorithm 1 Find a Stackelberg Multi-Defender Equilibrium (SMDE) input: a set of users U, Lu, L1 u(fu) and L0 u(fu) for every user u, and A for attacker return: a SMDE or there is no SMDE |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments that involve training on a dataset. While it mentions the UCI and Enron datasets in reference to prior work's tradeoff curves (Figure 1), it does not use them for its own experimental training or provide access information for this paper's work. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would involve training, validation, or test splits of data. |
| Hardware Specification | No | The paper does not provide any hardware specifications for running experiments, as it focuses on theoretical analysis and algorithm design. |
| Software Dependencies | No | The paper does not list any 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. |