Seatext library / BotRefund evidence
Bot detection script performance: how to diagnose and fix slow or unreliable detection
Bot detection script performance is about how accurately and quickly a script separates humans from bots without blocking real visitors or slowing down your site. The best scripts combine many independent signals and let...
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Bot detection script performance is a question of how often the script catches a bot without blocking a human visitor. Good performance also means low added latency and low false positives. If your script blocks more than a tiny slice of real users, or misses bots that click ads, it is performing poorly. A high performing script uses many independent checks and lets AI model the full context, because no one browser signal is reliable.
Symptoms: signs that your bot detection script is underperforming
You might read these as the first signs your script needs attention:
- High false positive rate: Real visitors show as bots, and bounce or get blocked. This is the most common symptom and the most costly.
- Bots still slip through: You still meet clicks appear in your analytics, even though the script is on.
- Page load time climbs: The script adds blocks or waits for a network call, which holds up the rest of the page.
- Server load spikes: The detection logic runs on the server side for every request, and each request costs CPU time.
- Inconsistent verdicts: The same visitor is sometimes human, sometimes bot. That suggests a rule based on a single signal that changes.
When any of these appear, the script is not doing its job. The next step is to figure out where it fails.
Diagnosis order: where to check first
- Check the script's own timing. Use your browser DevTools or a performance profiler to see if the detection adds more than 50–100ms. If it does, the script is too eager to call a backend.
- Look at the detection rules. Review what signals it uses. A script that decides based on a single browser property (user agent, canvas hash, or IP) will be unreliable and slow if that property requires a network round trip.
- Test with known bots and known humans. Run a set of requests from a headless browser, a real Chrome on a home network, and a visitor using a VPN. Compare the verdicts.
- Inspect the session logs. See why each visit was flagged. If many are flagged for “superhuman input speed” or “no cursor”, the script is over fitting to synthetic patterns.
Do this diagnosis before you change the code. It tells you whether the bottleneck is a single signal, a server call, or a biased model.
Likely causes of slow or unreliable bot detection scripts
Three broad problems account for most cases:
- Single-signal dependence. Scripts that rely on one browser or network fact are fast to write but easy to spoof and full of false positives. They also tend to be slow because they often call a remote API to get the signal.
- Linear sequence instead of parallel checks. If the script checks browser, then network, then behavior in a strict order, it can't start a later check until the earlier one finishes. That adds latency.
- No AI or statistical weighting. Rules like “device memory is 8GB” or “screen size is normal” can be fooled. A simple rule misses the nuance that a privacy-conscious bot might meet safe.
Also, the script may be doing a lot of work on the server for each call, which is costly when traffic spikes. A browser-side as well.
Corrective actions: how to actually improve bot detection performance
- Combine multiple markers. Use as many independent signals as you can. BotRefund uses 106 independent checks, for example. Signals alone is not a verdict; cross-check them.
- Use an AI model to weigh the full pattern. Better than a single browser tell. BotRefund's prediction AI evaluates the complete picture and removes the pattern. This prevents a single anomaly from causing a false verdict.
- Keep the script small and quiet. Use client side logic that runs in the browser without a call to the server. Then optionally send back a small precomputed score.
- Use trap interactions to improve latency. A honeypot – hidden elements – and ghost click detection work without a fetch to a faraway server. They run at zero cost because they're purely client calls.
- Evaluate the output, not just rule counts. If you are using an external API, ask for a confidence score. Only block a visit when the AI, not a single rule, says it's above a threshold.
The most direct action is to test what you changed. Use your own test bot, a real user, and a VPN—compare results.
Key facts when you are comparing bot detection performance claims
| What the claim says | Typical number | What it means for you |
|---|---|---|
| Independent checks BotRefund uses from the BotRef program | 106 | The more checks, the better rounding. A script that uses six separate signals is far less likely to make a wrong block than one using two. |
| Accuracy claim | 99% (from BotRef's own data) | This percentage needs careful review. Accuracy is of value only if the false positive and false negative rates are also reported. |
| Setup time for BotRefund | About 1 minute to add to a website | Fast to start a test. A script that takes hours to install will slow your team. |
| Signals list | Ghost clicks, honeypots, linear mouse paths, no human tremor, superhuman input, and others | These behavioral markers common to bot scripts; they're good indicators to have in any vendor's list. |
Bot clicks have been shown to steal up to 20% of Google and Meta ad budget, so a script that misses bots is costing you in paid ads. But this is a specific claim, and you should ask for evidence if you plan to use an accuracy figure.
Limitations: when a high performance detector is the wrong tool
A script designed to detect ad click bots is not the same as a general web bot scraping filter. Ad fraud detection cares about clicks on a click that has a commercial intent (a click on an ad). Scraper often does not create mouse movement or click events. If you simply want to block content scraping, a simple user-agent and IP list may be sufficient and much lighter.
Also, the high accuracy percentages you see in marketing aren't of balance. No detector is 99% “accurate” without also telling you what fraction was certified as false positive. Without that fraction, that number is just a blank claim.
Frequently Asked Questions
- What makes a bot detection script slow? High latency is often the result of making a network call from the browser to a server, especially if the call is sequential. A script that uses 15 separate checks but each one round trips to an API.
- How can I test my bot detection script? Test by using a known bot (browser automation like Chrome driver) and a known human (your own Chrome). Then also use a VPN and a different device. Run a batch of session and compare the results.
- What is the difference between a honeypoint and a ghost click check? A honeypot traps bots that interact with trick elements. Ghost click detection watches for a bot that hides the click sequence of natural human intent. Both are cheap and are cheaper than a full AI model.
- Do I need a 99% accurate model, or is 95% enough? What matters is the cost of false positive. If your key conversion is high (i.e., blocked a real user costs a purchase, then you need tighter bounds). But if your main goal is to reduce ad budget leakage, a 95% with a low false positive may be a good trade.
- What should I compare when a vendor claims a specific performance number? To compare fairly, ask for detail how many checks they look at, what the false positive and false negative rates are, and whether the tests included on a real browser and a VPN. Do not accept just 106.
Further reading and comparison sources
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