How does traffic analysis identify traffic anomalies?

Traffic analysis identifies traffic anomalies by monitoring network data patterns and detecting deviations from normal behavior. This involves collecting metrics like bandwidth usage, packet rates, connection counts, and protocol distributions over time to establish a baseline. When real-time traffic strays significantly from this baseline, it triggers alerts for potential anomalies.

Key techniques include:

1. Statistical Baseline Comparison: Establishing average traffic patterns (e.g., peak hours, typical data volumes) and flagging outliers. For example, a sudden 500% spike in outbound traffic during off-peak hours may indicate data exfiltration.
2. Protocol Analysis: Detecting unusual protocol usage. If HTTP traffic suddenly includes encrypted payloads at unusual volumes, it could suggest malware communication.
3. Behavioral Profiling: Tracking user/device behavior. A workstation suddenly generating DNS tunneling traffic (e.g., encoding data in DNS queries) is suspicious.

Example: A company’s web server normally handles 10,000 requests/minute. Traffic analysis tools detect a sudden surge to 50,000 requests/minute from a single IP, likely a DDoS attack.

For cloud environments, Tencent Cloud’s Network Security Solution provides real-time traffic monitoring, anomaly detection, and automated mitigation. Its DDoS Protection Service and Cloud Firewall integrate traffic analysis to block threats like port scanning or SQL injection attempts.