AnomBench: A Benchmark for Volume-Based Internet Anomaly Detection

Developing algorithms to detect anomalies in network traffic is an important goal to achieve secure and efficient operation of the Internet. To evaluate different algorithms, it is crucial to have a set of standardized test cases. We propose a benchmark suite called "AnomBench" that consists of sixteen different traffic scenarios that contain various different traffic anomalies. We describe why these scenarios are representative and show the results of a prototype implementation on DETER-lab.