When to Reset Your Keys: Optimal Timing of Security Updates via Learning
Authors: Zizhan Zheng, Ness Shroff, Prasant Mohapatra
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the advantages of our learning algorithms through numerical study. The results are averaged over 100 independent trials and are given in Figure 2. |
| Researcher Affiliation | Academia | Zizhan Zheng Department of Computer Science Tulane University Ness B. Shroff Dept. of ECE and CSE The Ohio State University Prasant Mohapatra Department of Computer Science University of California, Davis |
| Pseudocode | Yes | Algorithm 1 Improved UCB algorithm for time-associative bandits with side observations |
| Open Source Code | No | The paper does not provide a link to open-source code for the methodology nor does it explicitly state that the code is released. |
| Open Datasets | No | We use the following synthetic dataset. We assume that the attack time at follows an i.i.d. Weibull Distribution with CDF F(a) = 1 e (a/λ)k for a 0 and F(a) = 0 for a < 0. No access details are provided. |
| Dataset Splits | No | The paper does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In each trial, λ is chosen from the interval [1, 20] uniformly at random. We consider a 19 arm setting with xi evenly distributed in [1, 10] with a step size of 0.5. We consider both the binary loss function and the linear loss function mentioned in the model section. In both cases, we fix the defense cost to cd = 0.1. |