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Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis

Research article (ICC 2022 - IEEE International Conference on Communications, 2022) · cited 21× · AI/ML
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Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis

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Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis. Retrieved May 24, 2026, from https://4ort.xyz/entity/data-poisoning-attacks-against-autoencoder-based-anomaly-detection-models-a-robustness-analysis
MLA “Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/data-poisoning-attacks-against-autoencoder-based-anomaly-detection-models-a-robustness-analysis.
BibTeX @misc{4ortxyz_data-poisoning-attacks-against-autoencoder-based-anomaly-detection-models-a-robustness-analysis_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Data Poisoning Attacks against Autoencoder-based Anomaly Detection Models: a Robustness Analysis}}, year = {2026}, url = {https://4ort.xyz/entity/data-poisoning-attacks-against-autoencoder-based-anomaly-detection-models-a-robustness-analysis}, note = {Accessed: 2026-05-24}}
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