Detecting data anomalies from their formal specifications: a case study in IoT systems

Benjamin Aziz

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We present in this paper a new method in detecting anomalies in datasets representing systems behaviour, which is based on comparing a dataset to the data blueprint of the system representing its normal behaviour. This method removes some of the need for applying complex machine learning algorithms that aim at detecting abnormalities in such datasets and gives a more assured outcome of the presence of abnormalities. Our method models first a system using the formal langauge of the $\pi$-calculus, and then applies an abstract interpretation that ultimately generates an abstract multiset representing the messages exchanged in the system model. This multiset we term the data blueprint of the system, and it represents the normal behaviour expected. We apply this method to the case of a recent study in literature, which attempts to analyse normal and abnormal behaviour in datasets representing runs of the MQTT protocol, both under attack and no attack conditions. We show that our method is able to detect these conditions in an easier and more straightforward manner that the original case study attempts to.
Original languageEnglish
Article number630
Number of pages10
Issue number3
Publication statusPublished - 27 Jan 2023


  • Data Analysis
  • Process Algebra
  • IoT Systems
  • Formal Specifications
  • Data Anomalies

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