Making WAF ML models go brrr: saving decades of processing time

We made our WAF Machine Learning models 5.5x faster, reducing execution time by approximately 82%, from 1519 to 275 microseconds! Read on to find out how we achieved this remarkable improvement.
WAF Attack Score is Cloudflare's machine learning (ML)-powered layer built on top of our Web Application Firewall (WAF). Its goal is to complement the WAF and detect attack bypasses that we haven't encountered before. This has proven invaluable in catching zero-day vulnerabilities, like the one detected in Ivanti Connect Secure, before they are publicly disclosed and enhancing our customers' protection against emerging and unknown threats.
Since its launch in 2022, WAF attack score adoption has grown exponentially, now protecting millions of Internet properties and running real-time inference on tens of millions of requests per second. The feature's popularity has driven us to seek performance improvements, enabling even broader customer use and enhancing Internet security.
In this post, we will discuss the performance optimizations we've implemented for our WAF ML product. We'll guide you through specific code examples and benchmark numbers, demonstrating how these enhancements have significantly improved our system's efficiency. Additionally, we'll share the impressive latency reduction numbers observed after the rollout.
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