Publication at FMAS 2022

A new publication has been accepted by the FMAS 2022.

Bernhard K. Aichernig, Edi Mu┼íkardin, and Andrea Pferscher : “Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols.”, In Farrell, M., Luckcuck M. (eds.) Formal Methods for Autonomous Systems, FMAS 2022, Berlin, Germany, September 26-27, 2022.


Active automata learning became a popular tool for the behavioral analysis of communication protocols. The main advantage is that no manual modeling effort is required since a behavioral model is automatically inferred from a black-box system. However, several real-world applications of this technique show that the overhead for the establishment of an active interface might hamper the practical applicability. Our recent work on the active learning of Bluetooth Low Energy (BLE) protocol found that the active interaction creates a bottleneck during learning. Considering the automata learning toolset, passive learning techniques appear as a promising solution since they do not require an active interface to the system under learning. Instead, models are learned based on a given data set. In this paper, we evaluate passive learning for two network protocols: BLE and Message Queuing Telemetry Transport (MQTT). Our results confirm that a passive technique can correctly learn if the data set provides behavioral coverage. However, random data generation for passive learning is more expensive compared to the costs of active learning.