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Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy

: Schulze, Jan-Philipp; Mrowca, Artur; Ren, Elizabeth; Loeliger, Hans-Andrea; Böttinger, Konstantin


Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Knowledge Discovery and Data Mining -SIGKDD-:
KDD 2019, 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Proceedings : Anchorage, AK, USA, August 04 - 08, 2019
New York: ACM, 2019
ISBN: 978-1-4503-6201-6
International Conference on Knowledge Discovery & Data Mining (KDD) <25, 2019, Anchorage/Alaska>
Fraunhofer AISEC ()
anomaly detection; unsupervised learning; recurrent neural network; signal processing; automotive

Contextual anomalies arise only under special internal or external stimuli in a system, often making it infeasible to detect them by a rule-based approach. Labelling the underlying problem sources is hard because complex, time-dependent relationships between the inputs arise. We propose a novel unsupervised approach that combines tools from deep learning and signal processing, working in a purely data-driven way. Many systems show a desirable target behaviour which can be used as a proxy quantity removing the need to manually label data. The methodology was evaluated on real-life test car traces in the form of multivariate state message sequences. We successfully identified contextual anomalies during the cars' timeout process along with possible explanations. Novel input encodings allow us to summarise the entire system context including the timing such that more information is available during the decision process.