This talk is based on results of R&D project aimed to build a solution for user behavior security analytics. I will describe various methods and ideas for anomaly detection solutions built to understand user behavior trends and find abnormal activity using state-of-the-art neural networks.
The talk covers things like:
Empowering a feature selection process with clustering algorithms
Checking the quality of data with a serial correlation algorithm
Implementing a behavioral whitelisting with scoring analysis
Tuning a scoring engine with frequency analysis
Advancing scoring engine results with prefix coding
Predicting user actions with recurrent neural networks
