Cloudflare Bot Management: machine learning and more

The text discusses the development and implementation of a Cloudflare Bot Management platform. This platform combines distributed systems, web development, machine learning, security, and research to detect and manage bots effectively. It provides definitions for bots, good bots, bad bots, and bot management. The author outlines the requirements for this platform, including simplicity, trustworthiness, flexibility, accuracy, and recoverability. They also discuss the technical requirements, such as scalability, low latency, configurability, modifiability, and security. The text introduces the concept of a "Trusted Score" or "Score," which indicates the likelihood that a request originated from a human versus an automated program. This score is used in conjunction with Firewall Rules to provide customers with telemetry about bots on a per-request basis. The author provides an overview of the platform's architecture, including core bot management services and edge bot management modules. They also discuss integration with other Cloudflare products like WAF and Workers. The text delves into the five detection mechanisms currently used by the Cloudflare Bot Management platform: machine learning, heuristics engine, behavioral analysis, verified bots, and JS fingerprinting. Each of these mechanisms is explained in detail, along with their respective properties and benefits. The author concludes by acknowledging the hard work of various teams involved in building this platform and hints at future blog posts exploring the internals of detection mechanisms.