Network Traffic Analysis Techniques

Effective DDoS detection requires sophisticated traffic analysis capabilities. NetFlow and sFlow data provide visibility into traffic patterns without the overhead of full packet capture. These flow-based technologies summarize network conversations, enabling detection of traffic anomalies that indicate DDoS attacks. Analyze flow data for unusual source/destination patterns, protocol distributions, and packet sizes.

Deep packet inspection (DPI) offers detailed traffic analysis when flow data isn't sufficient. DPI examines packet contents to identify attack signatures, malformed packets, or protocol violations. While resource-intensive, DPI provides the detailed analysis needed to detect sophisticated application-layer attacks that might evade flow-based detection.

Statistical analysis helps identify deviations from normal traffic patterns. Establish baselines for typical traffic volume, protocol distribution, and geographic origins. Use statistical methods like standard deviation, moving averages, and seasonal trending to identify anomalies. Machine learning algorithms can learn normal patterns and automatically flag unusual activity.

Behavioral analysis goes beyond simple statistics to understand traffic context. Legitimate users exhibit predictable patterns: they request pages in logical sequences, spend time reading content, and interact with various site features. Attack traffic often lacks these human characteristics, requesting the same resources repeatedly or accessing pages in impossible sequences.