基于仿真推理的恶意活动检测和量化
Detecting and Quantifying Malicious Activity with Simulation-based Inference
摘要：
我们建议使用概率编程技术来解决推荐算法中的恶意用户识别问题。概率编程提供了优于其他技术的许多优点，包括但不限于提供恶意用户在结构化模型下如何行动的分解表示，以及允许量化恶意用户造成的损害。我们展示了恶意用户识别实验，使用一个简单的推荐算法进行交互的常规和恶意用户的模型，并提供了一种新的基于模拟的措施，用于量化用户或用户组对其动态的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
