Table of Contents
Monitoring network traffic is essential for maintaining security and performance. Quantitative techniques help identify unusual patterns that may indicate security threats or system issues. This article explores key methods used for anomaly detection in network traffic analysis.
Statistical Methods
Statistical techniques analyze traffic data to find deviations from normal behavior. Common methods include calculating averages, variances, and thresholds. When traffic exceeds these thresholds, it may signal an anomaly.
Machine Learning Approaches
Machine learning models can learn typical traffic patterns over time. Techniques such as clustering and classification help distinguish between normal and abnormal traffic. These models adapt to changing network conditions for improved detection accuracy.
Traffic Metrics and Indicators
Key metrics used in anomaly detection include:
- Packet rate: Number of packets per second
- Bandwidth usage: Data transfer volume
- Connection duration: Length of network sessions
- Source/destination diversity: Variety of IP addresses involved