ML to predict Load, Handle requests, Anamoly + Load Forecast

Mitigating DDoS with data science using AWS Shield Advanced and AWS WAF

• Time series - events are fed into a time-series database in near real time and generate insights using machine learning (ML) models
• Generating confidence percentage that helps in defining further action, verifies consumer authenticity, and serves the request. It also blocks malicious requests at the edge. Identify malicious patterns
• Forecast Load - Derive pattern-based rate limits: Deriving rate limits based on a larger set of data—including consumer and IP address—by looking at weekly and monthly patterns.

Black Friday/Cyber Monday [BFCM] is a big deal for our users. So it’s a big deal for @stripe too.

We handled billions of $s in sales each day of BFCM. A 5 minute downtime = tens of millions in lost revenue for our users.

Here's how we achieved >99.9999% uptime. 👇 https://t.co/eDYrgy5CWb

— David Singleton (@dps) November 29, 2022

• Our data science and eng teams build rigorous models based on historical data at both a Stripe-wide and user-by-user level.
• We build resilient systems to support spikes and flash sales, and scale our systems to handle more than the predicted peak.

How Razorpay handled significant transaction bursts during events like IPL

• Rate-limiting and throttling were implemented to safeguard their system against a deluge of requests also DDoS attacks
• The machine learning system consumed payment success and failure events to predict in real-time where the payment requests should be directed.

The Making of Developer-Console

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