DDoS Attack Detection

Build AI systems that accurately distinguish DDoS attacks from legitimate traffic surges, flash crowds, and seasonal peaks.

The Detection Challenge

The hardest part of DDoS defense is distinguishing attack traffic from legitimate surges. A viral marketing campaign, breaking news event, or product launch can cause traffic spikes that look similar to volumetric attacks. AI solves this by analyzing traffic behavior, not just volume.

Detection Features

Detection Approaches

• Entropy-based: Measure randomness of source IPs, ports, and packet sizes; DDoS often reduces entropy
• Rate-of-change: Monitor acceleration of traffic metrics, not just absolute values
• Autoencoder anomaly: Train on normal traffic patterns, high reconstruction error indicates attack
• Ensemble classification: Multiple models vote on whether traffic is attack or legitimate

Speed Requirements

DDoS detection must be fast to be useful:

• Volumetric attacks: Detect within 1-5 seconds (before bandwidth saturates)
• Protocol attacks: Detect within 5-15 seconds (before state tables fill)
• Application attacks: Detect within 15-60 seconds (before application resources exhaust)