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Anomaly Detection for Cybersecurity: Time Series Forecasting and Deep Learning

Authors: Giordano Colò

Finding anomalies when dealing with a great amount of data creates issues related to the heterogeneity of different values and to the difficulty of modelling trend data during time. In this paper we combine the classical methods of time series analysis with deep learning techniques, with the aim to improve the forecast when facing time series with long-term dependencies. Starting with forecasting methods and comparing the expected values with the observed ones, we will find anomalies in time series. We apply this model to a bank cybersecurity case to find anomalous behavior related to branches applications usage.

Comments: 32 Pages.

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Submission history

[v1] 2020-01-03 14:40:30

Unique-IP document downloads: 258 times

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