Sketch-based change detection: Methods, evaluation, and applications

Traffic anomalies such as failures and attacks are commonplace in today's network, and identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows that need to be examined for significant changes in traffic pattern (e.g., volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is either too expensive or too slow. We propose building compact summaries of the traffic data using the notion of sketches. We have designed a variant of the sketch data structure, k-ary sketch, which uses a constant, small amount of memory, and has constant per-record update and reconstruction cost. Its linearity property enables us to summarize traffic at various levels. We then implement a variety of time series forecast models (ARIMA, Holt-Winters, etc.) on top of such summaries and detect significant changes by looking for flows with large forecast errors. We also present heuristics for automatically configuring the model parameters. Using a large amount of real Internet traffic data from an operational tier-1 ISP, we demonstrate that our sketch-based change detection method is highly accurate, and can be implemented at low computation and memory costs. Our preliminary results are promising and hint at the possibility of using our method as a building block for network anomaly detection and traffic measurement.