A Taxonomy of Anomalies in Backbone Network Traffic

The potential threat of network anomalies on Internet has led to a constant effort by the research community to design reliable detection methods. Detection is not enough, however, because network administrators need additional information on the nature of events occurring in a network. Several works try to classify detected events or establish a taxonomy of known events. But, these works are non-overlapping in terms of anomaly type coverage. On the one hand, existing classification methods use a limited set of labels. On the other hand, taxonomies often target a single type of anomaly or, when they have wider scope, fail to present the full spectrum of what really happens in the wild. We thus present a new taxonomy of network anomalies with wide coverage of existing work. We also provide a set of signatures that assign taxonomy labels to events. We present a preliminary study applying this taxonomy with six years of real network traffic from the MAWI repository. We classify previously documented anomalous events and draw to main conclusions. First, the taxonomy-based analysis provides new insights regarding events previous classified by heuristic rule labeling. For example, some RST events are now classified as network scan response and the majority of ICMP events are split into network scans and network scan responses. Moreover, some previously unknown events now account for a substantial number of all UDP network scans, network scan responses and port scans. Second, the number of unknown events decreases from 20 to 10% of all events with the proposed taxonomy as compared to the heuristic approach.