Platform intrusion detection with deep learning

shop.rewe.de is visited over 2 million times each day. Every visitor is producing thousands of requests in our micro service architecture. We are trying to give the best shopping experience for our customers, and try to keep our platform safe from bad bots. This is partly done by rule sets, and furthermore done by a platform that uses machine learning to classify bad behaviour. In this talk I would like to present our architecture that is not only able to fulfill this use case, but enables our data scientists in general to use our big data platform. the main scope will be the presentation of the use case. I will cover: different microservices written in python using flask, google bigquery, tensorflow and keras scaling this microservices with kubernetes, that automatically starts more predictors in case of higher load implementation of quantifiers written in python, that generates data suitable for neural networks using numpy examples from the real world behaviour of this plattform with lessons learned examples how we feature engineered the data by analysing the data stored