What's the most cost-effective way to detect bot traffic using an invalid traffic detection system at scale?

The most cost-effective approach is usually a layered, rules-first system with selective ML, rather than trying to classify everything with expensive models in real time.

Best practical pattern

1. Cheap pre-filters at the edge

   • Block obvious bad traffic with:
     • IP reputation / ASN allowlists-denylists
     • Rate limiting
     • Geo anomalies
     • User-agent sanity checks
     • Header/cookie consistency checks
   • This removes a large share of junk very cheaply.

2. Event-based detection on sampled or suspicious traffic

   • Don’t inspect every event deeply.
   • Send only:
     • suspicious sessions
     • high-value impressions/clicks
     • randomly sampled baseline traffic
   • This keeps compute and storage costs down while preserving detection quality.

3. Feature aggregation instead of raw-event analysis

   • Build session/device/IP features like:
     • clicks per minute
     • session depth
     • repeat patterns
     • fingerprint stability
     • impossible navigation paths
   • Aggregate in streaming systems so you avoid expensive per-event inference.

4. Use lightweight anomaly/rules models first

   • Start with:
     • heuristics
     • decision trees / gradient-boosted models
     • simple anomaly scoring
   • Reserve heavier ML or graph analysis for high-risk clusters only.

5. Batch retraining, real-time scoring

   • Train models offline on labeled data.
   • Deploy small, fast models for live scoring.
   • This is much cheaper than continuous online learning at scale.

6. Feedback loop from adjudication

   • Use human-reviewed invalid traffic findings and advertiser disputes to improve rules and labels.
   • Better labels reduce false positives, which is often the biggest hidden cost.

What usually makes it cheapest

• Filter early, inspect late
• Sample aggressively
• Aggregate features
• Use deterministic rules for the easy cases
• Apply ML only where the signal is ambiguous

Common cost traps to avoid

• Scoring every request with a large model
• Storing all raw logs forever
• Overusing graph computations on all traffic
• Excessive false positives that require manual review
• Too many bespoke rules that become hard to maintain

If you want a simple default architecture

• CDN/WAF edge rules for first-pass blocking
• Stream processor to build session features
• Rules engine for obvious fraud
• Small ML model for ambiguous cases
• Human review for high-value or disputed cases
• Periodic model/rule retraining

If you want, I can also outline a reference architecture, cost model, or build-vs-buy comparison for this system.