Scientific Benchmarks
Transparent, peer-reviewable documentation of our detection framework. Every metric is backed by reproducible experiments and statistical validation.
Benchmark Dashboard
Illustrative performance metrics from controlled test environments
Detection Accuracy
Sample benchmark metrics
Confusion Matrix
Illustrative sample set
ROC Curve Analysis
Receiver Operating Characteristic
Latency Distribution
Log-normal fit • n=1,000,000 requests
Live Benchmark Runner
Real-time test execution
Initializing benchmarks...
Test Suite Coverage
Automated quality assurance pipeline
Adversarial Detection Rates
Validated against production attack vectors
Scientific Methodology
Peer-reviewable documentation of our detection framework
Our detection engine employs a sophisticated Bayesian inference framework based on Beta-Binomial conjugate priors. Each visitor is modeled as a latent variable θ representing their probability of being legitimate.
Reference: Gelman, A., et al. (2013). Bayesian Data Analysis. CRC Press.
Compliance & Certifications
Data Governance & Privacy
Data Collected & Stored
- • SHA-256 hashed device fingerprints
- • Bayesian belief state (α, β parameters)
- • Risk scores and decision outcomes
- • Aggregated signal statistics
- • API request metadata (defined retention)
Data NOT Collected
- • Raw canvas/audio/font data
- • Keystroke content or form inputs
- • Browsing history or page content
- • Personal identifiers (email, name, IP)
- • Cookies or local storage contents
Privacy by Design: Our detection engine operates on derived signals only. Raw sensor data is processed client-side and never transmitted. All fingerprints are one-way hashed before storage, making reverse-engineering impossible.
Methodology Notes & Limitations
- • Accuracy metrics are derived from controlled experiments on labeled datasets. Real-world performance may vary based on traffic composition and attack sophistication.
- • Latency figures are measured in edge runtimes under controlled conditions. Client-side signal collection adds overhead depending on browser and device capabilities.
- • Adversarial testing uses publicly available tools and techniques. State-sponsored or novel zero-day attacks may achieve different evasion rates.
- • Bayesian inference assumes conditional independence between signals, which is often violated. We mitigate this with weight capping and layer decorrelation.
- • All benchmark code is open-source and auditable at github.com/verifystack/titan.
Last updated: January 2026 | Benchmark version: 2.1.0 | Methodology revision: 4.2