Anomaly Detection in OKTA Logs using Autoencoders

• URL: http://arxiv.org/abs/2411.07314v1
• Date: Mon, 11 Nov 2024 19:15:05 GMT
• Title: Anomaly Detection in OKTA Logs using Autoencoders
• Authors: Jericho Cain, Hayden Beadles, Karthik Venkatesan,
• Abstract summary: Okta logs are used to detect cybersecurity events using various rule-based models with restricted look back periods. These functions have limitations, such as a limited retrospective analysis, a predefined rule set, and susceptibility to generating false positives. We adopt unsupervised techniques, specifically employing autoencoders.
• Score: 0.0
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• Abstract: Okta logs are used today to detect cybersecurity events using various rule-based models with restricted look back periods. These functions have limitations, such as a limited retrospective analysis, a predefined rule set, and susceptibility to generating false positives. To address this, we adopt unsupervised techniques, specifically employing autoencoders. To properly use an autoencoder, we need to transform and simplify the complexity of the log data we receive from our users. This transformed and filtered data is then fed into the autoencoder, and the output is evaluated.

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