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Backdoor Attack against One-Class Sequential Anomaly Detection Models
In this paper, we explore compromising deep sequential anomaly detection models by proposing a novel backdoor attack strategy.
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations
Given a sequence that is detected as anomalous, we can consider anomalous entry detection as an interpretable machine learning task because identifying anomalous entries in the sequence is to provide an interpretation to the detection result.
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