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Anomaly detection in a forensic timeline with deep autoencoders

Studiawan, H. and Sohel, F. (2021) Anomaly detection in a forensic timeline with deep autoencoders. Journal of Information Security and Applications, 63 . Art. 103002.

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An investigator needs to analyze a forensic timeline after a cybersecurity incident has occurred. Log entries from various sources are used to generate a forensic timeline. Finding the anomalous activities recorded in these log records is a difficult task if manual inspection or keyword searches are used. In this work, we propose a method for identifying anomalies in a forensic timeline. We use deep autoencoders as a machine learning technique to establish a baseline for normal activities in log files. Furthermore, we set an anomaly threshold of reconstruction value based on the constructed baseline. We then plot these anomalous events on a forensic timeline. Our experiments indicate that the proposed method achieves superior performance compared to other log anomaly detection methods with overall mean F1 score and accuracy of 94.036% and 96.720%, respectively.

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Publisher: Elsevier Ltd
Copyright: © 2021 Published by Elsevier Ltd.
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