Antibot

Antibot refers to techniques and systems designed to distinguish automated software (bots) from human users and to limit or block automated access to online services. The goal is to protect websites, APIs, and digital services from abuse such as scraping, credential stuffing, fraud, account takeover, spam, and service disruption, while allowing legitimate automated interactions (e.g., search engines, APIs with proper access).

Core components include bot detection and bot mitigation. Detection uses signals from client and server: browser fingerprinting, JavaScript challenges, mouse and keyboard behavior, device and IP reputation, rate limiting, and historical anomaly scoring. Some systems employ CAPTCHA or other challenges as a verification step. Modern approaches rely on machine learning to classify traffic and adapt to evolving bot capabilities.

Deployment often involves integration with web application firewalls, gateways, or dedicated bot-management platforms. They may operate at the edge (CDN/WAF) or within application layers, and can apply passive verification or active responses such as challenge pages, session throttling, or outright blocking. They also distinguish between shields against bad bots and allowing good bots with proper access.

Challenges and considerations include user experience, false positives, and the risk of bots evolving to bypass defenses. Privacy concerns and regulatory compliance (data protection) are important, as is the potential impact on accessibility and legitimate business automation. Effective antibot strategies typically combine multiple signals, regular updates, and ongoing monitoring.

Related concepts include bot management, CAPTCHA, web application firewall, rate limiting, and API security.