Case Study: StubHub achieves drastic reduction in bot-driven fraud and halves unwanted bot traffic with Distil Networks

StubHub - Customer Case Study

StubHub, an eBay-owned online marketplace and the world’s largest secondary ticket platform processing nearly 500 transactions per second, was facing large-scale bot attacks — from brute-force account takeovers to competitors scraping prices and monitoring inventory — that caused resource spikes and degraded site performance. Multiple vendor solutions proved hard to configure and in some cases broke the site, so StubHub required a bot mitigation solution that would accurately separate good bots from bad, block scrapers without impacting human visitors, self-tune with machine learning, leverage a community threat database, and integrate seamlessly with existing CDN/WAF/SIEM stacks.

StubHub deployed Distil Networks’ machine-learning driven bot detection and mitigation, using negative and positive security models and a centralized violators database to block malicious actors while whitelisting trusted sources. The result was a drastic reduction in competitive data mining, improved SEO and marketplace protection, a 50% reduction in pageviews without affecting human users or ad delivery, and materially improved traffic quality — making Distil a core part of StubHub’s fraud prevention suite.

StubHub