The objective of this research is to create a data architecture and a state-of-the-art machine learning algorithms to build a robust user-profile system that (i) extracts, stores, builds, and analyses synchronously up to 1 million user profiles generating at least 50 behavioral data (alpha-numeric value of 64 bytes) per second, (ii) provides over 5 millions user-profile recognitions per day through predictive modeling and REST API call, (iii) authenticates continuously to detect suspicious activities and anomalies without using cookies, location, and hardware information, and (iv) tolerates ef