# Detecting and Quantifying Malicious Activity with Simulation-based Inference

arXiv: https://arxiv.org/abs/2110.02483 · HTML: https://mlsanity.com/paper/2110.02483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [An Introduction to Probabilistic Programming](https://mlsanity.com/paper/1809.10756/) (2018)
- [Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale](https://mlsanity.com/paper/1907.03382/) (2019)
- [The History of Digital Spam](https://mlsanity.com/paper/1908.06173/) (2019)
- [Spacecraft Collision Risk Assessment with Probabilistic Programming](https://mlsanity.com/paper/2012.10260/) (2020)

## Abstract (arXiv)

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.