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Active automata learning became a popular tool for the behavioral analysis ofcommunication protocols. The main advantage is that no manual modeling effortis required since a behavioral model is automatically inferred from a black-boxsystem. However, several real-world applications of this technique show thatthe overhead for the establishment of an active interface might hamper thepractical applicability. Our recent work on the active learning of BluetoothLow Energy (BLE) protocol found that the active interaction creates abottleneck during learning. Considering the automata learning toolset, passivelearning techniques appear as a promising solution since they do not require anactive interface to the system under learning. Instead, models are learnedbased on a given data set. In this paper, we evaluate passive learning for twonetwork protocols: BLE and Message Queuing Telemetry Transport (MQTT). Ourresults show that passive techniques can correctly learn with less data thanrequired by active learning. However, a general random data generation forpassive learning is more expensive compared to the costs of active learning.

Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols