Detecting and Quantifying Malicious Activity with Simulation−based Inference
Andrew Gambardella‚ Bogdan State‚ Naeemullah Khan‚ Kleovoulos Tsourides‚ Philip Torr and Atılım Güneş Baydin
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.