Global Bigdata Conference

There is more value in using multistage machine-learning analysis and actual data in an effort to determine which machine learning model will work best for detecting real security events on any one particular network. Processing data streams from various subsystems (data transmission frequency measurements over time, for instance, or protocols in a network stream that identify affiliated applications and infrastructure devices) using a variety of machine learning models, and then comparing the learned data to the original raw data, lets an enterprise grade each data stream to reveal which models provide the highest predictability of anomaly detection for that distinct network. Machine learning models may run the gamut from associated rules learning, to sparse dictionary learning, to Bayesian fields and artificial neural networks.