I went straight to feature engineering, things like click patterns, movement speed, session length, and tried to sketch out a pipeline from data collection to model serving.
Start by clarifying the scope—what constitutes a bot, what data is available, and what the business impact is. Then propose a layered detection system combining rule-based heuristics, supervised ML models, and unsupervised anomaly detection, with a feedback loop for continuous improvement. Emphasize trade-offs between precision and recall, latency, and scalability, and how you'd measure success.
Pro tip: Frame the problem as an adversarial arms race: bots evolve, so your system must adapt. Mention the importance of human-in-the-loop review and how you'd handle false positives to avoid punishing legitimate players.
Ask questions to understand what types of bots (e.g., aimbots, farming bots, spam bots) and what data is available (game logs, player reports, etc.). Define success metrics like precision, recall, and business impact.
Identify relevant data sources: player actions, session patterns, device fingerprints, network traffic. Engineer features that capture bot-like behavior, such as action frequency, timing regularity, and social interactions.
Choose a combination of approaches: rule-based filters for known bots, supervised models (e.g., gradient boosting) using labeled data, and unsupervised anomaly detection (e.g., isolation forests) for novel bots. Consider deep learning for sequence data.
Design a scalable pipeline for real-time scoring, with low-latency inference. Integrate with game servers to take actions (e.g., flag, ban, shadowban) and ensure the system can handle high throughput.
Set up A/B tests and offline evaluation. Monitor model drift and adversarial adaptation. Incorporate human review and player reports to create a feedback loop for retraining and improving the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.