A Sketch-Based Algorithm for Network-Flow Entropy Estimation on Programmable Switches Using P4

The empirical Shannon entropy is a popular metric for anomaly detection in network traffic. However, computing its exact value in real time requires fast access to a large number of counters, which is unfeasible in high-speed networks. Approximate approaches using sketches can estimate the entropy with low memory usage. However, achieving good estimation accuracy still requires large data structures, making their implementation difficult in dedicated hardware and programable switches. In this paper, we present an entropy-estimation algorithm and its implementation in a programmable switch, which achieves good accuracy for large traffic traces with low memory usage. The algorithm uses sketches to track the packet count of only the most-frequent flows and models the rest of the traffic with a uniform distribution. The implementation operates within the restrictions imposed by the P4 switch programming language, achieving a 1.72% average estimation error on 12 real-world large traces from public repositories.