Optimal Personalized Filtering Against Spear-Phishing Attacks
Authors: Aron Laszka, Yevgeniy Vorobeychik, Xenofon Koutsoukos
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our results using two real-world datasets and demonstrate that the proposed thresholds lead to lower losses than nonstrategic thresholds. |
| Researcher Affiliation | Academia | Aron Laszka and Yevgeniy Vorobeychik and Xenofon Koutsoukos Institute for Software Integrated Systems Department of Electrical Engineering and Computer Science Vanderbilt University Nashville, TN |
| Pseudocode | Yes | Theorem 2. Suppose that we are given a constant Λ, and the defender s choice is restricted to strategies where maxu A fu Lu Λ and minu A fu Lu Λ for a best response A2. Then, the output of the following algorithm is an optimal defense strategy: 1. For each user u, compute the loss of user u when it is not targeted as follows: ... 7. Output the strategy f. |
| Open Source Code | No | The paper does not provide concrete access to its own source code, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | UCI The first dataset is from the UCI Machine Learning Repository (Bache and Lichman 2013)... Enron The second dataset is the Enron e-mail dataset3 (Klimt and Yang 2004). 3http://www.cs.cmu.edu/ ./enron/ |
| Dataset Splits | No | The paper specifies training and testing splits (80% training, 20% testing for UCI; 12,000 training, 1,500 testing for Enron) but does not mention a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions training a 'na ıve Bayes classifier' but does not provide specific version numbers for any software, libraries, or dependencies used in the experiments. |
| Experiment Setup | Yes | For every user, potential losses due to undelivered non-malicious and delivered targeted malicious e-mails are approximately ten times higher than losses due to delivered non-targeted e-mails. Formally, for each user u, Lu, Cu 10 Nu. The potential damage values Lu, Cu, and Nu follow a power law distribution. Formally, the number of users with damage values between some l and l + 1 is approximately twice as much as the number of users with values between l + 1 and l + 2. Finally, the value of Lu ranges from 0.5 to 5.5. |