Advancing Threat Intelligence: JA4 fingerprints and inter-request signals
For many years, Cloudflare has used advanced fingerprinting techniques to help block online threats, in products like our DDoS engine, our WAF, and Bot Management. For the purposes of Bot Management, fingerprinting characteristic elements of client software help us quickly identify what kind of software is making an HTTP request. It’s an efficient and accurate way to differentiate a browser from a Python script, while preserving user privacy. These fingerprints are used on their own for simple rules, and they underpin complex machine learning models as well.
Making sure our fingerprints keep pace with the pace of change on the Internet is a constant and critical task. Bots will always adapt to try and look more browser-like. Less frequently, browsers will introduce major changes to their behavior and affect the entire Internet landscape. Last year, Google did exactly that, making older TLS fingerprints almost useless for identifying the latest version of Chrome.
JA3 Fingerprint
JA3 fingerprint introduced by Salesforce researchers in 2017 and later adopted by Cloudflare, involves creating a hash of the TLS ClientHello message. This hash includes the ordered list of TLS cipher suites, extensions, and other parameters, providing a unique identifier for each Continue reading








