Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 75
  • Download : 0
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures. Unfortunately, existing approaches fall too short for these challenges; online anomaly detection methods bear the burden of handling the complexity while offline deep anomaly detection methods suffer from the evolving data distribution. This paper presents a framework for online deep anomaly detection, ARCUS, which can be instantiated with any autoencoder-based deep anomaly detection methods. It handles the complex and evolving data streams using an adaptive model pooling approach with two novel techniques: concept-driven inference and drift-aware model pool update; the former detects anomalies with a combination of models most appropriate for the complexity, and the latter adapts the model pool dynamically to fit the evolving data streams. In comprehensive experiments with ten data sets which are both high-dimensional and concept-drifted, ARCUS improved the anomaly detection accuracy of the streaming variants of state-of-the-art autoencoder-based methods and that of the state-of-the-art streaming anomaly detection methods by up to 22% and 37%, respectively.
Publisher
Association for Computing Machinery
Issue Date
2022-08-16
Language
English
Citation

28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, pp.2347 - 2357

DOI
10.1145/3534678.3539348
URI
http://hdl.handle.net/10203/298758
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0