论文标题
使用ML增强模拟训练可靠的异常检测
Training robust anomaly detection using ML-Enhanced simulations
论文作者
论文摘要
本文介绍了使用神经网络来增强对随后训练异常检测系统的模拟。模拟可以为现实数据中可能稀疏或不存在异常检测提供边缘条件。但是，通过产生“太干净”的数据，导致无法从模拟数据过渡到实际条件的数据。我们的方法使用对现实世界数据进行培训的神经网络增强了模拟，以创建比传统仿真更现实和可变的输出。
This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Simulations suffer, however, by producing data that is "too clean" resulting in anomaly detection systems that cannot transition from simulated data to actual conditions. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations.
