A framework for real-time worm attack detection and backbone monitoring

Abstract

We developed an open source Internet backbone monitoring and traffic analysis framework named UPFrame. It captures UDP NetFlow packets, buffers it in shared memory and feeds it to customised plug-ins. UPFrame is highly tolerant to misbehaving plug-ins and provides a watchdog mechanism for restarting crashed plug-ins. This makes UP-Frame an ideal platform for experiments. It also features a traffic shaper for smoothing incoming traffic bursts. Using this framework, we have investigated IDS-like anomaly detection possibilities for high-speed Internet backbone networks. We have implemented several plug-ins for host behaviour classification, traffic activity pattern recognition, and traffic monitoring. We successfully detected the recent Blaster, Nachi and Witty worm outbreaks in a medium-sized Swiss Internet backbone (AS559) using border router NetFlow data captured in the DDoSVax project. The framework is efficient and robust and can complement traditional intrusion detection systems.