BlockABot uses a layered detection system combining behavioral signals, browser fingerprinting, and shared threat intelligence to identify and reduce automated traffic. The system is designed to operate in real time with minimal impact to legitimate users.
IPs identified as malicious are added to a shared threat pool and can be blocked across all protected sites. This enables fast response to repeated attacks and known bad actors.
Known malicious IPs can be blocked before full request processing using a lightweight middleware layer, reducing server load and exposure.
Traffic patterns observed across multiple sites help identify repeat offenders and persistent automated activity.
Each request is evaluated using a weighted scoring system that considers automation indicators, behavior patterns, and fingerprint consistency.
Detects high-frequency requests, bursts of traffic, and abnormal navigation behavior commonly associated with bots.
Recognizes common browser signals such as plugins and standard headers to reduce false positives and allow legitimate users.
Identifies headless environments and rendering engines such as SwiftShader commonly used in automation.
Detects browser automation frameworks including Selenium and WebDriver through fingerprint signals.
Flags command-line and scripted clients such as curl, Python requests, and non-browser traffic.
Uses rendering differences and GPU signals to identify emulated or automated environments.
Detects mismatches between device signals such as screen size, CPU cores, touch capability, and platform.
Identifies incomplete or inconsistent fingerprints that often indicate automation or spoofing.
Identifies traffic originating from cloud providers and infrastructure commonly used for automation.
Flags missing or abnormal HTTP headers such as Accept and Accept-Language that are often absent in bots.
Detects suspicious or inconsistent user agent strings associated with scripted traffic.
Validates that visitors can execute JavaScript, helping filter out non-browser and automated clients.
Blocks common malicious scans targeting sensitive paths such as /wp-admin, /.env, and system files.
Detects automated form submissions using hidden fields designed to trap bots.
Logs request data including bot scores, fingerprint signals, and detection reasons for analysis.
Provides visibility into traffic volume, blocked threats, detection sources, and top attackers.
Access a continuously updated list of high-activity bot and scraper IPs detected across the network. Premium plans can use this data for monitoring or external security integrations.
Identify fingerprints reused across multiple IP addresses to detect proxy rotation and distributed bot networks.
Introduce graduated responses such as verification challenges for suspicious traffic instead of immediate blocking.
Analyze longer session patterns and navigation sequences to detect more complex automated activity.
BlockABot is designed to reduce automated traffic and abuse. No system can eliminate all bots or guarantee complete protection.