SESSION
Exploring Anomalies in Authentication Logs with Autoencoders
OVERVIEW
EXPERIENCE | In Person |
---|---|
TYPE | Breakout |
TRACK | Data Science and Machine Learning |
INDUSTRY | Enterprise Technology |
TECHNOLOGIES | AI/Machine Learning, Apache Spark |
SKILL LEVEL | Advanced |
DURATION | 40 min |
DOWNLOAD SESSION SLIDES |
Authentication 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.
SESSION SPEAKERS
Jericho Cain
/Sr Staff Security Data Scientist
Adobe
Hayden Beadles
/Sr. Security Machine Learning Engineer
Adobe