Improving the accuracy of our machine learning WAF using data augmentation and sampling

At Cloudflare, we are always looking for ways to make our customers' faster and more secure. A key part of that commitment is our ongoing investment in research and development of new technologies, such as the work on our machine learning based Web Application Firewall (WAF) solution we announced during security week.

In this blog, we’ll be discussing some of the data challenges we encountered during the machine learning development process, and how we addressed them with a combination of data augmentation and generation techniques.

Let’s jump right in!

Introduction

The purpose of a WAF is to analyze the characteristics of a HTTP request and determine whether the request contains any data which may cause damage to destination server systems, or was generated by an entity with malicious intent. A WAF typically protects applications from common attack vectors such as cross-site-scripting (XSS), file inclusion and SQL injection, to name a few. These attacks can result in the loss of sensitive user data and damage to critical software infrastructure, leading to monetary loss and reputation risk, along with direct harm to customers.

How do we use machine learning for the WAF?

The Cloudflare ML solution, at a high level, Continue reading