Impact of Adversarial Machine Learning Against Anomaly Detectors in Time Series

• Felipe Dallmann Tomazeli UEL
• Gilberto Fernandes Junior UEL
• Bruno Bogaz Zarpelão UEL

DOI: https://doi.org/10.5753/sbseg.2024.241416

Abstract

Anomaly detection can be employed in time series to automatically identify faults, outages, and misuse in devices, services, and systems. Machine learning algorithms have been successfully applied to detect anomalies in time series of various natures. However, these algorithms are vulnerable to Adversarial Machine Learning attacks, which can result in anomalies not being detected, or normal situations being erroneously detected as anomalies, generating false positives. In light of this reality, this work investigates how attacks based on adversarial examples can impact an anomaly detection model based on a Long Short-Term Memory (LSTM) neural network. Within the scope of this study, two methods of generating adversarial examples are tested: one based on the addition of noise calculated over the standard deviation and another based on the Fast Gradient Sign Method (FGSM) technique. The results showed that the anomaly detection model experiences a decrease in its predictive capability when attacked, but outperforms a classifier based on a Multi-layer Perceptron (MLP) neural network under the same conditions.

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