A classic IP blacklist is no longer enough against bot traffic; bot operations rotate IP pools every week and constantly evolve their behaviour. A static list expires within five days.
wall.click delivers a two-layer approach: (1) automatic detection of bot behaviour through machine-learning models that are continuously retrained, (2) a manual rule engine tailored to your industry and business logic. Both work side-by-side and both are managed from a single dashboard.
What you gain
What you get with this solution
ML-based detection
A model trained on behavioral fingerprint, click pattern and session signals; retrained weekly.
Custom rule engine
Define your own no-code rules combining IP, ASN, geography, frequency and behavior.
Whitelist & blacklist
Permanently pass safe IPs, permanently block suspicious ones; CIDR-block and ASN-based list management.
Sensitivity levels
5 levels from conservative to aggressive; you can apply different levels per campaign.
Headless browser detection
Catch automation tools like Puppeteer, Selenium and Playwright through fingerprint signals.
Coordinated-attack detection
Detect coordinated movement of different IPs concentrated on the same campaign.
Problem
The bot ecosystem keeps evolving
Roughly 30-50% of internet traffic is bot traffic (Imperva Bad Bot Report 2024). Half of that is malicious: scrapers, click bots, attribution-fraud bots and more sophisticated variants. Bot operators ship new techniques weekly; static filters lose their effectiveness within 1-2 weeks.
Where classic bot detection falls short
- User-Agent checks — bots send realistic UAs
- JavaScript challenges — modern bots execute JS
- Static IP blacklists — bot networks rotate IPs every week
- CAPTCHA — destroys UX and loses real customers
- Single-signal filtering — easy to bypass
Modern bot categories
Today's bot landscape is far broader: classic scrapers, humanlike bots that simulate real mouse trajectories, residential proxy networks, and click farms run by real human operators.
Method
Our layered detection approach
- 1
Layer 1: Machine learning
Our Gradient Boosting + LSTM model is trained on millions of labelled sessions. It correlates behavioural sequences, mouse trajectory, scroll patterns and intra-session consistency. The model is retrained weekly as new bot variants emerge. - 2
Layer 2: Manual rule engine
A no-code rule editor for industry- or business-specific patterns the ML model can't catch on its own. Write complex AND/OR conditions like "IF IP outside Turkey AND 5+ clicks/hour AND not mobile → block". - 3
Layer 3: Community intelligence
A new bot operation found at one customer is anonymously propagated across the platform. You benefit from a database of 2.5M+ known bad IPs refreshed hourly.
Data
Bot categories we catch and their distribution
Categorical breakdown of bots detected by wall.click over the last 90 days:
41%
Classic scrapers / crawlers
Caught via User-Agent + behaviour sequencing
27%
Headless browser bots
Puppeteer, Selenium, Playwright fingerprints
19%
Residential proxy + humanlike
Real human behaviour simulated through home IPs
The remaining 13% splits across categories: click farms, coordinated attacks, attribution-fraud bots, SDK spoofing.
Practice
Custom rule examples
Rule sets our customers commonly use (deployable from the dashboard with one click):
Frequency limit
Geographic boundary
Datacenter ASN
Device mismatch
Campaign sensitivity
Whitelist priority
FAQ