Learning-Based Fuzzing of IoT Message Brokers

Aichernig, Bernhard K.; Muškardin, Edi; Pferscher, Andrea

doi:10.1109/icst49551.2021.00017

Cited by 20 publications

(9 citation statements)

References 24 publications

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“…AALpy has been successfully used to learn the protocols of MQTT and Bluetooth. These learned models serve as a basis for learningbased testing [3] and fuzzing [5].…”

Section: Discussionmentioning

confidence: 99%

“…We particularly focus on learning models of reactive systems, whose input-output behavior under appropriate abstraction can be captured by regular languages. Learning models of such systems in the MAT framework lends itself nicely to a test-based implementation, as demonstrated in various case studies [5,11,30,35]. Algorithms in this framework alternate between two phases.…”

Section: Aalpy -Intuitive Automata Learning In Pythonmentioning

confidence: 97%

“…In particular, we experienced a performance benefit of using PyPy over CPython. 5 Learning of deterministic models. The efficiency of AALpy for learning deterministic models was evaluated with extensive experiments on random automata.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Considering the differences in the behavioral models, a fingerprinting sequence could be generated that uniquely identifies the BLE device. In future work, the learned models can be used to generate a stateful blackbox fuzzing technique as proposed by Aichernig et al [5].…”

Section: Fuzzing Bluetooth Low Energymentioning

confidence: 99%

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AALpy: an active automata learning library

Muškardin

Aichernig

Pill

et al. 2022

Innovations Syst Softw Eng

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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.

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“…AALpy has been successfully used to learn the protocols of MQTT and Bluetooth. These learned models serve as a basis for learningbased testing [3] and fuzzing [5].…”

Section: Discussionmentioning

confidence: 99%

Section: Aalpy -Intuitive Automata Learning In Pythonmentioning

confidence: 97%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Fuzzing Bluetooth Low Energymentioning

confidence: 99%

See 2 more Smart Citations

AALpy: an active automata learning library

Muškardin

Aichernig

Pill

et al. 2022

Innovations Syst Softw Eng

Self Cite

View full text Add to dashboard Cite

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“…However, this app :1" oach ne � ds a white-box component inside the SUT to y1eld sensible test cases. In order to overcome this, there is an approach that uses abstract automata leaming to leam an abstract model of the SUT, and performs conformance testing on the SUT using fuzzing techniques for concretizing the test input [15]. On the other hand, models can be subjected to a symbolic model checker, which is an already elaborated field [16], that would yield attacks for components that could also serve as a basis for an attack graph.…”

Section: Displays An Example Model With Matched Vulnerabilities In Th...mentioning

confidence: 99%

A Model-Driven Methodology for Automotive Cybersecurity Test Case Generation

Marksteiner

Priller

2021

2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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Through international regulations (most promi nently the latest UNECE regulation) and standards, the already widely perceived higher need for cybersecurity in automotive systems has been recognized and will mandate higher efforts for cybersecurity engineering. The UNECE also demands the effectiveness of these engineering to be verified and validated through testing. This requires both a significantly higher rate and more comprehensiveness of cybersecurity testing that is not effectively to cope with using current, predominantly manual, automotive cybersecurity testing techniques. To allow for comprehensive and efficient testing at all stages of the automotive life cycle, including supply chain parts not at band, and to facilitate efficient third party testing, as well as to test under real-world conditions, also methodologies for testing the cybersecurity of vehicular systems as a black box are necessary. This paper therefore presents a model and attack tree-based approach to (semi-)automate automotive cybersecurity testing, as well as considerations for automatically black box-deriving models for the use in attack modeling.

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