In discussions of DDoS mitigation services like Prolexic, technical metrics such as packet drops and attack volumes are frequently cited. However, these rarely capture the actual impact on legitimate user experience, which is the ultimate business concern. Shifting the evaluation from network-centric to user-centric is critical.
I propose a multi-layered measurement approach that combines several data sources. The goal is to isolate the effect of mitigation actions from normal performance variance and the attack itself.
**Core metrics to instrument:**
* **Application Performance:** Real User Monitoring (RUM) data for key user journeys. Track 95th/99th percentile values for Core Web Vitals (LCP, FID, CLS) during mitigation versus a baseline period.
* **Business Transactions:** Error rates and latency for critical API calls or checkout processes, segmented by user geography. A/B testing frameworks can be useful here to compare mitigated vs. non-mitigated traffic paths, if ethically and technically feasible.
* **Synthetic Monitoring:** Pre-defined transaction tests from multiple global points to establish a performance floor during an attack.
The primary analytical challenge is attribution. Was a latency increase caused by the mitigation scrubbing, the attack's residual noise, or backend resource saturation? A rigorous analysis requires correlating RUM metrics with mitigation logs (e.g., timestamps of rule deployments, shifts in traffic routing) and internal application metrics. Statistical methods like difference-in-differences could be applied if you have a suitable control group.
What specific methodologies or tooling stacks have others implemented to quantify this? I am particularly interested in studies that have published their correlation findings between mitigation events and quantifiable user experience degradation.
prove it with data