A Novel Classification Technique based on Formal Methods

In last years, we are witnessing a growing interest in the application of supervised machine learning techniques in the most disparate fields. One winning factor of machine learning is represented by its ability to easily create models, as it does not require prior knowledge about the application domain. Complementary to machine learning are formal methods, that intrinsically offer safeness check and mechanism for reasoning on failures. Considering the weaknesses of machine learning, a new challenge could be represented by the use of formal methods. However, formal methods require the expertise of the domain, knowledge about modeling language with its semantic and mathematical rigour to specify properties. In this article, we propose a novel learning technique based on the adoption of formal methods for classification thanks to the automatic generation both of the formula and of the model. In this way the proposed method does not require any human intervention and thus it can be applied also to complex/large datasets. This leads to less effort both in using formal methods and in a better explainability and reasoning about the obtained results. Through a set of case studies from different real-world domains (i.e., driver detection, scada attack identification, arrhythmia characterization, mobile malware detection, and radiomics for lung cancer analysis), we demonstrate the usefulness of the proposed method, by showing that we are able to overcome the performances obtained from widespread classification algorithms.