Bot detection is the set of techniques used to determine whether a request to a website or service originates from a real human or from an automated program (bot).
The share of bot traffic on the internet is staggering. According to Imperva's 2024 report, approximately 49.6% of all web traffic comes from bots. Roughly 32% of that is malicious - scraping, credential stuffing, DDoS attacks, and more.
For URL shortening services, bot detection is critical. Fraudulent bot clicks corrupt click analytics. If a campaign's shortened URL is mass-clicked by bots, the actual user count is overestimated and marketing decisions are made on false data.
Primary detection methods include behavioral analysis (mouse movements, scroll patterns, click intervals), fingerprint analysis (whether browser characteristics match typical human profiles), rate analysis (abnormally high request frequency from a single IP), JavaScript challenges (filtering bots that cannot execute JavaScript), and CAPTCHAs (challenges only humans can solve).
Sophisticated bots mimic human behavior, so no single method is sufficient. A scoring model that combines multiple signals is far more effective. Services like Cloudflare Bot Management, AWS WAF Bot Control, and reCAPTCHA Enterprise provide machine-learning-based bot detection. You can find related books on Amazon.