Bot & Fraud Defense
Stop Bots, Scrapers, and Fraud Before They Cost You
Signal: bot
Challenge automated behavior
01
Requirements reviewed with your team
02
Technical fit and integrations assessed
03
Pilot scope defined around measurable outcomes
The problem
Automated traffic doesn't announce itself. It shows up as login attempts, checkout flows, and API calls that look almost like a real user, at a volume and speed no human generates — and a naive IP block just teaches the operator behind it to rotate IPs, which is exactly what services like MaskFlare Cloak exist to do for legitimate use, and what unmanaged scraping and credential-stuffing tools already do for illegitimate use.
Blocking on IP reputation alone increasingly means blocking nothing, since residential and rotating proxy networks make the attacking traffic look like it's coming from ordinary consumer connections. Effective defense has to score behavior and device signal instead of relying on the network address a request arrives from.
How MaskFlare Radar works
Radar scores every request in real time using behavioral signals, device and browser fingerprinting, and risk modeling, and applies your policy — allow, challenge, or block — before the request reaches your application, not after a fraud team investigates it days later.
Benefits
Behavior-based, where IP reputation falls short
Requests are scored on how they behave, so rotating IPs and residential proxy traffic don't get a free pass just because the source address looks clean.
Stops credential stuffing before account takeover
High-velocity login attempts against your own leaked-credential lists are challenged or blocked before an attacker finds a valid pair.
Covers checkout and API endpoints too
The same detection engine covers checkout bots, scraper traffic, and API abuse, alongside account login.
Frictionless for real users
Legitimate traffic is scored and passed through without a CAPTCHA wall in the way, reserving friction for requests that actually score as risky.
Capabilities
Behavioral biometrics
Mouse movement, typing cadence, and interaction patterns distinguish a scripted client from a human one, even when the network signal looks identical.
Device and browser fingerprinting
The same fingerprinting technique Cloak uses to mask a session, applied in reverse to identify automation and repeat abusive devices attempting to look like new visitors.
ML-based risk scoring
Every request receives a risk score from a model trained on abuse patterns, feeding an allow, challenge, or block decision your policy controls.
Explore related capabilities
See how the capabilities planned for MaskFlare address adjacent security and privacy requirements.
Related from Flarepedia
Frequently asked questions
What is bot mitigation?
Bot mitigation is the practice of detecting and controlling automated traffic to a website, app, or API, distinguishing it from real human visitors so unwanted automation can be challenged or blocked without harming real users.
What is credential stuffing?
Credential stuffing is an attack where previously leaked username-and-password pairs are tried automatically against a login page at scale, betting that some users reused the same password elsewhere.
How is Radar different from a simple rate limit?
A rate limit only counts requests per IP over time. Radar scores behavior and device signal, so it still catches distributed, low-and-slow, or rotating-IP attacks a rate limit alone would miss.
Will Radar block legitimate scrapers or partners?
Policy is configurable per endpoint, so known partner integrations, monitoring services, and search engine crawlers can be explicitly allow-listed alongside automated defense for everything else.
Does Radar require changing our application code?
Radar sits in front of your traffic at the network layer for most integrations; API-level scoring is available through a lightweight SDK where deeper request context is useful.
Your next chapter starts here
Make room for possibility.
We'll talk protection.
Tell us what your team needs to protect.
Let's explore where MaskFlare could fit.