Multi-granular aggregation of network flows for security analysis
Tao Ding, Ahmed F. AlEroud, George Karabatis · 2015
Investigating network flows is an approach of detecting attacks by identifying known patterns. Flow statistics are used to discover anomalies by aggregating network traces and then using machine-learning classifiers to discover suspicious activities. However, the efficiency and effectiveness of the flow classification models depends on the granularity of aggregation. This paper describes a novel approach that aggregates packets into network flows and correlates them with security events generated by payload-based IDSs for detection of cyber-attacks.