Detecting and Quantifying Malicious Activity with Simulation-based
Inference
- URL: http://arxiv.org/abs/2110.02483v2
- Date: Thu, 7 Oct 2021 10:56:01 GMT
- Title: Detecting and Quantifying Malicious Activity with Simulation-based
Inference
- Authors: Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides,
Philip H. S. Torr, At{\i}l{\i}m G\"une\c{s} Baydin
- Abstract summary: We show experiments in malicious user identification using a model of regular and malicious users interacting with a recommendation algorithm.
We provide a novel simulation-based measure for quantifying the effects of a user or group of users on its dynamics.
- Score: 61.9008166652035
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose the use of probabilistic programming techniques to tackle the
malicious user identification problem in a recommendation algorithm.
Probabilistic programming provides numerous advantages over other techniques,
including but not limited to providing a disentangled representation of how
malicious users acted under a structured model, as well as allowing for the
quantification of damage caused by malicious users. We show experiments in
malicious user identification using a model of regular and malicious users
interacting with a simple recommendation algorithm, and provide a novel
simulation-based measure for quantifying the effects of a user or group of
users on its dynamics.
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