How I Built a Real-Time DDoS Detection Engine from Scratch

The detection logic you've described is a sophisticated approach to identifying potential attacks based on statistical anomalies and error rates. Here's a summary of the key components:

1. Sliding Window for Rate Calculation:

   • Tracks the rate of requests per second (req/s) from each IP address.
   • Uses a sliding window mechanism to ensure that only recent data is considered.

2. Baseline Calculation:

   • Maintains a baseline of normal traffic patterns using historical data.
   • Adapts dynamically by giving more weight to recent hourly data if available, ensuring the system can adapt to changes in traffic patterns over time.

3. Z-Score Detection:

   • Computes how many standard deviations away from the mean an IP's current rate is.
   • If the z-score exceeds 3.0, it flags a statistically anomalous event.

4. Spike Multiplier:

   • Checks if the current request rate is more than five times the baseline mean rate.
   • This helps catch sudden spikes in traffic that might indicate an attack even when the variance is high.

5. Error Rate Monitoring:

   • Tracks error rates (fraction of 4xx/5xx responses) for each IP over

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