BlockABot uses a multi-layered detection engine combining behavioral analysis, fingerprint intelligence, and real-time anomaly detection to accurately separate humans from bots while minimizing false positives.
Combines browser fingerprinting, behavioral signals, and anomaly detection into a unified scoring system for highly accurate bot identification.
Each visitor is scored using weighted signals including automation indicators, request behavior, and fingerprint anomalies to determine allow or block actions.
Recognizes real user characteristics such as browser plugins and standard headers to reduce false positives and protect legitimate traffic.
Detects headless browsers such as Headless Chrome used by scraping tools and automation frameworks.
Identifies browser automation frameworks including Selenium and WebDriver commonly used in scripted attacks.
Flags known scripting environments such as curl, Python clients, and scraping libraries attempting to access your site.
Uses rendering differences and GPU detection (including SwiftShader) to identify headless and emulated environments.
Detects mismatches between screen size, CPU cores, touch capability, language, and timezone — common indicators of spoofed environments.
Identifies incomplete or inconsistent browser fingerprints commonly associated with automated or emulated environments.
Detects high-frequency request patterns and sudden traffic bursts commonly associated with scraping bots and automated attacks.
Evaluates network characteristics and traffic patterns to help identify automated or infrastructure-based access.
Detects missing referrers and inconsistent navigation patterns that may indicate non-human browsing behavior.
Flags missing or malformed HTTP headers such as Accept and Accept-Language that are often absent in automated requests.
Detects spoofed or conflicting user agents that indicate non-standard or scripted environments.
Ensures browser, device, and network signals align with real-world usage patterns.
Requires execution of browser-based JavaScript to validate real users and block non-browser traffic.
Detects automated form submissions using invisible fields designed to trap bots.
Automatically allows or blocks traffic based on calculated bot risk score.
Logs all traffic including fingerprint signals, bot scores, and request metadata for full visibility.
Identify top attacking IPs and suspicious behavior patterns across your sites.
Monitor bot activity and traffic patterns live across protected applications.
Detect fingerprints reused across multiple IPs to expose proxy rotation and distributed bot networks.
Introduce graduated responses including challenge flows for suspicious traffic instead of immediate blocking.
Expanded detection using shared signals and evolving bot behavior patterns across protected sites.