Major project Cybersecurity & Digital Trust Case Studio

Bot and attack traffic scoring

Inspired by publicly reported work at Cloudflare

Score network traffic from 1 (attack) to 99 (human) in real time, and decide what to block, challenge or allow.

The brief

Cloudflare has described its bot management and web application firewall using machine learning to give each request a score, which site owners use to block, challenge or allow traffic. Build your own traffic scorer on public intrusion datasets: engineer flow features, train a model, turn it into a 1–99 score, set block and challenge bands, and serve it in real time. Good looks like: detection and false-alarm rates per attack type, a calibrated score, bands justified by cost, a streaming demo and an evaluation on a second dataset.

Read the case first

What Cloudflare has said in public about this kind of work, on its own pages:

Make it your own

Protect one kind of site: a college results page on result day, a ticket booking site or a government form portal. Decide what a wrongly blocked real user costs there and set the bands for it.

AITeky has no link with Cloudflare, which has not reviewed or endorsed this brief. The brief only draws on what the company has published, uses open data rather than its data, and is a learning exercise: don't present your project as Cloudflare's work or system.

What you will build

Explore flows and attack types
Engineer flow features and remove leaking ones
Train a model and calibrate a 1–99 score
Set block, challenge and allow bands
Test on a second dataset to check it generalises
Serve scores on a stream with a monitoring dashboard

Free, open data to build it with

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