Learning-Based Fuzzing of IoT Message Brokers

Bernhard Aichernig, Edi Muskardin, Andrea Pferscher

    Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review

    Abstract

    The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities. We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.
    Original languageEnglish
    Title of host publicationProceedings - 2021 IEEE 14th International Conference on Software Testing, Verification and Validation, ICST 2021
    Place of PublicationBrazil
    Pages47
    Number of pages58
    ISBN (Electronic)978-1-7281-6836-4
    Publication statusPublished - 12 Apr 2021

    Fingerprint

    Dive into the research topics of 'Learning-Based Fuzzing of IoT Message Brokers'. Together they form a unique fingerprint.

    Cite this