Learning Abstracted Non-deterministic Finite State Machines

Innovations Syst Softw Eng

Pill

et al. 2022

Self Cite

AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning, as well as on an intuitive and seamlessly integrated interface for learning automata characterizing real-world reactive systems. In this article, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance. Finally, we present selected case studies on learning models of various types of systems with AALpy.

Section: Aalpy Provides Two Algorithms For Learning Observable Non-de...mentioning

confidence: 99%

AALpy: an active automata learning library

Muškardin

Innovations Syst Softw Eng

Pill

et al. 2022

Self Cite

“…The broker publishes the will of a client when the client disconnects. In the literature, we find several case studies [36,1,27] which applied active automata learning to learn behavioral models of different MQTT-broker implementations. The learned behavioral models revealed inconsistencies in the MQTT specification.…”

Section: Message Queuing Telemetry Transportmentioning

confidence: 99%

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

Electron. Proc. Theor. Comput. Sci.

Muškardin

Pferscher

2022

Self Cite

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 show that passive techniques can correctly learn with less data than required by active learning. However, a general random data generation for passive learning is more expensive compared to the costs of active learning.

“…Instead of workarounds to overcome nondeterministic behavior, we could learn a non-deterministic model. We already applied non-deterministic learning to the MQTT protocol [28]. Following a similar idea, we could learn a non-deterministic model of the BLE protocol.…”

Section: Future Workmentioning

confidence: 99%

Fingerprinting Bluetooth Low Energy Devices via Active Automata Learning

Pferscher

Lecture Notes in Computer Science

2021

Self Cite

Automata learning is a technique to automatically infer behavioral models of black-box systems. Today's learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite recent improvements in the scalability of learning algorithms, their practical applicability is still an open issue. Little work exists that actually learns models of physical black-box systems. To fill this gap in the literature, we present a case study on applying automata learning on the Bluetooth Low Energy (BLE) protocol. It shows that not only the size of the system limits the applicability of automata learning. Also, the interaction with the system under learning creates a major bottleneck that is rarely discussed. In this article, we propose a general automata learning architecture for learning a behavioral model of the BLE protocol implemented by a physical device. With this framework, we can successfully learn the behavior of six investigated BLE devices. Furthermore, we extended the learning technique to learn security critical behavior, e.g., key-exchange procedures for encrypted communication. The learned models depict several behavioral differences and inconsistencies to the BLE specification. This shows that automata learning can be used for fingerprinting black-box devices, i.e., characterizing systems via their specific learned models. Moreover, learning revealed a crashing scenario for one device.