Learning-Based Fuzzing of IoT Message Brokers

Bernhard Aichernig, Edi Muskardin, Andrea Pferscher

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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
Number of pages12
ISBN (Electronic)978-1-7281-6836-4
Publication statusPublished - Apr 2021
Event2021 IEEE International Conference on Software Testing: ICST 2021 - Virtuell, Brazil
Duration: 12 Apr 202116 Apr 2021

Publication series

NameProceedings - 2021 IEEE 14th International Conference on Software Testing, Verification and Validation, ICST 2021


Conference2021 IEEE International Conference on Software Testing
Abbreviated titleICST 2021


  • Internet of Things
  • Learning automata
  • Protocols
  • Fuzz testing
  • MQTT
  • Model inference
  • Model-based testing
  • automata learning
  • model inference
  • active automata learning
  • stateful fuzzing
  • conformance testing
  • IoT

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality

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