According to Imperva's 2024 Bad Bot Report, 49.6% of internet traffic is bot traffic; half of that (32%) falls into the malicious bot category. For advertisers, that means a meaningful share of every click you pay for comes from automated systems, not humans.
Bot traffic is not homogeneous. It ranges from simple crawlers to sophisticated ML-driven bot networks that simulate real user behaviour. wall.click uses a multi-layer detection architecture that catches each bot type with the right signals.
What you gain
What you get with this solution
Real-time detection
Bot behavior (sub-second clicks, pattern scroll, missing interaction) is caught instantly.
Automatic blocking
Detected bot IPs are added to your Google Ads negative list automatically; the same attack won't return.
Constantly updated model
As new bot types appear, the ML model is retrained weekly.
Suspicious ASN list
Datacenter ASNs that host bot traffic are blocked in bulk.
Community intelligence
A new bot operation detected for one customer is propagated anonymously across the platform.
Headless browser detection
Catch Puppeteer, Selenium and Playwright through fingerprint signals.
Typology
Bot traffic categories
The bot ecosystem evolves constantly and categories overlap. The core classification:
1. Classic scrapers / crawlers
Automated scripts that collect data from websites. Apart from legitimate search-engine crawlers (Googlebot, Bingbot), most exist for competitive price monitoring, content copying or competitor intelligence. They may click your ads accidentally or on purpose.
2. Click bot services
Organized bot networks run by black-market "click vendors". They sell paid click services to fraudsters and competitors who want to attack specific ads. They typically operate through datacenter or residential proxies.
3. Attribution-fraud bots
These exploit the "last-click attribution" model in mobile advertising. Techniques like click flooding (millions of random clicks) or click injection (injecting fake clicks during app install) let them steal credit for organic installs.
4. Headless browser bots
Bots written with browser automation tools like Puppeteer, Selenium and Playwright. They run full JavaScript and look like real browsers; they bypass classic User-Agent checks.
5. Stress-test and scraping bots
Bots used for site load tests or competitor content copying. Even if they don't click your ads, they distort impression counts of PPC ad slots on your site.
6. Ad-verification bots
Bots from ad-verification platforms (DoubleVerify, Integral Ad Science) that check ads appear in the correct contexts. Legitimate, but they should not be counted as clicks; wall.click filters them automatically.
Data
The real volume of bot traffic
49.6%
Share of internet traffic that is bots
Imperva Bad Bot Report 2024
32%
Share that is malicious bots
One third of total traffic
73%
Share of advanced/sophisticated bots
Modern bots that bypass classic detection
Method
wall.click's bot detection approach
- 1
Behavioural fingerprinting
Mouse trajectory, keyboard interaction, scroll speed and on-page duration. Real human behaviour shows natural variance; bots tend toward consistent patterns. - 2
Device fingerprinting
navigator.webdriver flag, missing browser APIs (Puppeteer traces, for example), browser plugins, GPU info, screen-resolution inconsistencies. - 3
Network signals
IP reputation (proxy, VPN, datacenter ASN), port profile, TLS handshake fingerprint, IPv4/IPv6 consistency. - 4
Click-series analysis
Click series from the same IP (frequency, time-of-day regularity); the same fingerprint appearing across different IPs (proxy chain). - 5
Correlated scoring
30+ signals are evaluated together — no single signal is enough. Sessions above the threshold are auto-blocked.
Practice
What happens when a bot is detected?
Automatic IP block
ASN-wide block
Community database
Reporting
Whitelist option
ML model updates
FAQ