Anomaly Detection in OKTA Logs using Autoencoders

Autor:	Cain, Jericho, Beadles, Hayden, Venkatesan, Karthik
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 3 tables, 8 figures, Databricks AI Summit 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.07314 Zobrazit plný text záznamu View this record from Arxiv