AI-Driven Rate Limiting for Scalable and Cost-Efficient APIs
summary
Describes an AI rate limiter that ingests over 14 data points and synthesizes 27 behavioral features, trains a hybrid model (random forest committee + gradient‑boosting “investigator”) and runs as serverless (AWS Lambda/Azure Function) for microsecond inference. Claims: 97.5% model accuracy, 96% malicious detection, 68% drop in false positives and ~27% infrastructure cost savings. Implements a 0–100 threat confidence with progressive throttling (500 ms delays for medium risk, stricter limits or block for high risk) and a phased rollout: audit, data pipeline, pilot, continuous retrain.
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chapters (14)
- 0:00Introduction and Welcome
- 0:21The Importance of Rate Limiting
- 0:58Challenges with Traditional Rate Limiting
- 1:44The Need for Intelligent Rate Limiting
- 2:08Understanding the Current API Landscape
- 2:42Flaws in Static Rate Limiting
- 7:12Modern Threats: DDoS Attacks
- 9:26Introducing AI-Powered Rate Limiting
- 11:14Data Collection for AI Models
- 17:40Engineering Features for AI Models
- 24:13Training the AI Model
- 29:04Deploying the AI Model
- 34:35Real-World Results and Advanced Strategies
- 38:16Conclusion and Contact Information
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