I’ve spent 10 years building bots that bypass anti-fraud systems. Now I fight them by building anti-bot detection systems - and most defenses don’t work.
In this article, I’ll break down how human-like bot traffic actually works - and show a simple way to make bots click on hidden links.
In Russia, bot traffic is highly industrialized
Almost every website receives large volumes of “direct” and “referral” visits that are not real users. These visits distort analytics and can negatively impact rankings in Yandex (a Russian search engine like Google).
There is a fundamental difference between bot traffic patterns in Russia and global markets:
In Russia, bots are primarily used to manipulate behavioral signals - pushing sites higher in search results without paying for ads.
In global markets, bots are optimized for revenue - ad fraud, lead fraud, affiliate abuse.
Same tools. Different goals.
Why Russian-style bots matter
Russian bot operators are highly focused on mimicking real user behavior.
And you don’t need to be a developer to build these bots.
In Russia, tools like Browser Automation Studio or ZennoPoster allow users to create bots visually - like building a flowchart:
- move the mouse to a specific element
- click elements
- fill out forms
An example of a bot created visually in ZennoPoster - no coding required:
A dynamic captcha is much harder to solve: no text labels, no fixed HTML paths, no fixed CSS classes, no fixed element positions:
The goal of human-like bots is not to take a site down, but to look like real users. Because of that, you can’t detect them using simple signals like IP address, user agent, language, region, screen resolution, or similar parameters.
Detecting this type of traffic requires a completely different approach.
On this approach, I built my own anti-bot system
It focuses not on user parameters, but on what software generates bot traffic. Each bot is created by specific software, and that software produces a unique snapshot. This snapshot does not depend on browser parameters inside the session.
My snapshot is not a fingerprint. A fingerprint is a set of browser parameters; a snapshot is the bot-generating program identifier.
Tools like BAS or anti-detect browsers like MoreLogin produce their own snapshots that differ from real browsers - and that’s exactly how human-like bots can be detected.
For bot visualization, I use Yandex Metrica - it’s a free and powerful web analytics tool. It’s especially useful when analyzing traffic quality.
You can see how the bot clicks a hidden link in the session recording shown in the video attached to this article. The bot thinks it is on a normal website, but it is actually on a captcha page. It first moves the mouse over the captcha, then moves the cursor to the top of the page - where hidden links are placed to look like a navigation menu.
You can also test it on your own traffic:
👉 https://my.kill-bot.net/
From my experience, most analytics and bot detection systems completely miss this type of traffic.
In the next post I’ll explain how search rankings in Google are manipulated by bots
Have you ever checked whether bots click elements on your site that real users can’t even see?
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