We propose a novel near real-time method for early detection of worm outbreaks in high-speed Internet backbones. Our method attributes several behavioural properties to individual hosts like ratio of outgoing to incoming traffic, responsiveness and number of connections. These properties are used to group hosts into distinct behaviour classes. We use flow-level (Cisco NetFlow) information exported by the border routers of a Swiss Internet backbone provider (AS559/SWITCH). By tracking the cardinality of each class over time and alarming on fast increases and other significant changes, we can early and reliably detect worm outbreaks. We successfully validated our method with archived flow-level traces of recent major Internet email based worms such as MyDoom.A and Sobig.F, and fast spreading network worms like Witty and Blaster. Our method is generic in the sense that it does not require any previous knowledge about the exploits and scanning method used by the worms. It can give a s...