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Anomaly Detection in Unstructured Time Series Datausing an LSTM Autoencoder
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

An exploration of anomaly detection. Much work has been done on the topic of anomalyd etection, but what seems to be lacking is a dive into anomaly detection of unstructuredand unlabeled data. This thesis aims to determine the efctiveness of combining recurrentneural networks with autoencoder structures for sequential anomaly detection. The use of an LSTM autoencoder will be detailed, but along the way there will also be backgroundon time-independent anomaly detection using Isolation Forests and Replicator Neural Networks on the benchmark DARPA dataset. The empirical results in this thesis show that Isolation Forests and Replicator Neural Networks both reach an F1-score of 0.98. The RNN reached a ROC AUC score of 0.90 while the Isolation Forest reached a ROC AUC of 0.99. The results for the LSTM Autoencoder show that with 137 features extracted from the unstructured data, it can reach an F1 score of 0.8 and a ROC AUC score of 0.86

Abstract [sv]

En undersökning av anomalitetsdetektering. Mycket arbete har gjorts inom ämnet anomalitetsdetektering, men det som verkar saknas är en fördjupning i anomalitetsdetektering av ostrukturerad och omärktdata. Denna avhandling syftar till att bestämma effektiviteten av att kombinera Recurrent Neural Networks med Autoencoder strukturer för sekventiell anomalitetsdetektion. Användningen av en LSTM autoencoder kommeratt beskrivas i detalj, men bakgrund till tidsoberoende anomalitetsdetektering med hjälp av Isolation Forests och Replicator Neural Networks på referens DARPA dataset kommer också att täckas. De empiriska resultaten i denna avhandling visar att Isolation Forestsoch Replicator Neural Networks (RNN) båda når en F1-score på 0,98. RNN nådde en ROC AUC-score på 0,90 medan Isolation Forest nådde en ROC-AUC på 0,99. Resultaten för LSTM Autoencoder visar att med 137 features extraherade från ostrukturerad data kan den nå en F1-score på 0,80 och en ROC AUC-score på 0,86.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:303
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-231368OAI: oai:DiVA.org:kth-231368DiVA, id: diva2:1225367
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2018-08-24 Created: 2018-06-26 Last updated: 2018-08-24Bibliographically approved

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CiteExportLink to record
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