Training robust anomaly detection using ML-Enhanced simulations

• URL: http://arxiv.org/abs/2008.12082v2
• Date: Thu, 5 Nov 2020 10:56:59 GMT
• Title: Training robust anomaly detection using ML-Enhanced simulations
• Authors: Philip Feldman
• Abstract summary: Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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