[2106.03837] MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

Given a stream of entries over time in a multi-aspect data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events in an offline fashion and require a large amount of data for training. This is not practical in real-life scenarios where we receive the data in a streaming manner and do not know the size of the stream beforehand. Thus, we need a data-efficient method that can detect and adapt to changing data trends, or concept drift, in an online manner. In this work, we propose MemStream, a streaming multi-aspect anomaly detection framework, allowing us to detect unusual events as they occur while being resilient to concept drift. We leverage the power of a denoising autoencoder to learn representations and a memory module to learn the dynamically changing trend in data without the need for labels. We prove the optimum memory size required for effective drift handling.

1 mentions: @siddharthb_
Keywords: 異常検知
Date:

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@siddharthb_
@siddharthb_ Excited about our accepted paper at @TheWebConf "MemStream: Memory-Based Streaming Anomaly Detection" Preprint: t.co/91si9Lp6zL Code: t.co/Csv3kIJU8X #TheWebConf #WWW2022 #TheWebConf2022 t.co/PQJfbsmdQO

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