An Architectural Blueprint for a Multi-purpose Anomaly Detection on Data Streams

Christoph Augenstein, Norman Spangenberg, Bogdan Franczyk

2019

Abstract

Anomaly detection means a hypernym for all kinds of applications finding unusual patterns or not expected behaviour like identifying process patterns, network intrusions or identifying utterances with different meanings in texts. Out of different algorithms artificial neuronal nets, and deep learning approaches in particular, tend to perform best in detecting such anomalies. A current drawback is the amount of data needed to train such net-based models. Moreover, data streams make situation even more complex, as streams cannot be directly fed into a neuronal net and the challenge to produce stable model quality remains due to the nature of data streams to be potentially infinite. In this setting of data streams and deep learning-based anomaly detection we propose an architecture and present how to implement essential components in order to process raw input data into high quality information in a constant manner.

Paper Citation

in Harvard Style

Augenstein C., Spangenberg N. and Franczyk B. (2019). An Architectural Blueprint for a Multi-purpose Anomaly Detection on Data Streams.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-372-8, pages 470-476. DOI: 10.5220/0007760404700476

in Bibtex Style

@conference{iceis19,
author={Christoph Augenstein and Norman Spangenberg and Bogdan Franczyk},
title={An Architectural Blueprint for a Multi-purpose Anomaly Detection on Data Streams},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2019},
pages={470-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007760404700476},
isbn={978-989-758-372-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Architectural Blueprint for a Multi-purpose Anomaly Detection on Data Streams
SN - 978-989-758-372-8
AU - Augenstein C.
AU - Spangenberg N.
AU - Franczyk B.
PY - 2019
SP - 470
EP - 476
DO - 10.5220/0007760404700476