To design a bot detection system, we'll address both real-time online and offline detection. For online detection, with 1 million daily active users and a 2-second latency requirement, we'll focus on low-latency feature extraction and real-time model inference. Offline detection, processing 2 billion activity logs daily, allows for more complex analysis and model retraining.
Data and Labeling: We'll use human-labeled data to identify bot players. Given the likely imbalance (few bots compared to humans), we'll employ techniques like re-weighting and re-sampling. To augment our dataset, we'll develop a bot simulator to generate synthetic data, assigning it a lower weight than human-labeled data during training.
Feature Engineering: Features will be derived from various sources:
- Real-time signals: Low-latency features extracted during online gameplay (e.g., input patterns, reaction times, movement consistency).
- Historical activity: Aggregated user behavior over time (e.g., session duration, game progression, purchase history).
- Network and device information: IP address reputation, device fingerprinting, user agent analysis.
- Behavioral patterns: Deviations from typical human play, repetitive actions, unnatural click sequences, or exploit usage.
- Simulated data features: Characteristics specific to our bot simulator.
Model Selection: We'll use a combination of models:
- Online: Lightweight models like logistic regression or shallow neural networks for real-time scoring.
- Offline: More complex models such as gradient boosting machines (e.g., XGBoost, LightGBM) or deep learning models (e.g., LSTMs for sequential data) for deeper analysis and retraining.
System Architecture:
- Data Pipeline: A robust pipeline to ingest and process both real-time event streams and batch logs.
- Feature Store: A centralized store for computed features, accessible by both online and offline systems.
- Model Serving: A low-latency serving infrastructure for online models.
- Training Pipeline: An automated pipeline for retraining models with new data and simulator outputs.
- Monitoring and Alerting: Dashboards to track detection rates, false positives/negatives, and system performance, with alerts for anomalies.
Evaluation Metrics: We'll prioritize precision and recall, focusing on minimizing false positives (banning legitimate players) while maximizing true positives (catching bots). AUC and F1-score will also be monitored.
Roblox