The Odd One: Applying Machine Learning to User Behavior Anomaly Analysis


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
  • Adaptive threshold for RNN predictions
  • Generating synthetic but realistic datasets
  • Peer group analysis and other interesting ideas.

Location: Track 2 Date: April 12, 2018 Time: 5:30 pm - 6:30 pm Eugene Neyolov