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This paper explores the problem of determining which classes of Petri nets can be encoded into behaviourally-equivalent CCS processes. Most of the existing related literature focuses on the inverse problem (i.e., encoding process calculi belonging to the CCS family into Petri nets), or extends CCS with Petri net-like multi-synchronisation (Multi-CCS). In this work, our main focus are free-choice and workflow nets (which are widely used in process mining to describe system interactions) and our target is plain CCS. We present several novel encodings, including one from free-choice workflow nets (produced by process mining algorithms like the alpha-miner) into CCS processes, and we prove that our encodings produce CCS processes that are weakly bisimilar to the original net. Besides contributing new expressiveness results, our encodings open a door towards bringing analysis and verification techniques from the realm of process calculi into the realm of process mining., Comment: 38 pages, 18 figures, extended version of paper to be published at COORDINATION 2024

Attack Trees are a graphical model of security used to study threat scenarios. While visually appealing and supported by solid theories and effective tools, one of their main drawbacks remains the amount of effort required by security experts to design them from scratch. This work aims to remedy this by providing a method for the automatic generation of Attack Trees from attack logs. The main original feature of our approach w.r.t existing ones is the use of Process Mining algorithms to synthesize Attack Trees, which allow users to customize the way a set of logs are summarized as an Attack Tree, for example by discarding statistically irrelevant events. Our approach is supported by a prototype that, apart from the derivation and translation of the model, provides the user with an Attack Tree in the RisQFLan format, a tool used for quantitative risk modeling and analysis with Attack Trees. We illustrate our approach with the case study of attacks on a communication protocol, produced by a state-of-the-art protocol analyzer.

We propose a novel methodology for validating software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). Our approach focuses on the feature-oriented language QFLan in the PL engineering domain, allowing modeling of PLs with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs like staged configurations. This richness leads to models with infinite state-space, requiring simulation-based analysis techniques like SMC. For instance, we illustrate with a running example involving infinite state space. SMC involves generating samples of system dynamics to estimate properties such as event probabilities or expected values. On the other hand, PM uses data-driven techniques on execution logs to identify and reason about the underlying execution process. In this paper, we propose, for the first time, applying PM techniques to SMC simulations' byproducts to enhance the utility of SMC analyses. Typically, when SMC results are unexpected, modelers must determine whether they stem from actual system characteristics or model bugs in a black-box manner. We improve on this by using PM to provide a white-box perspective on the observed system dynamics. Samples from SMC are fed into PM tools, producing a compact graphical representation of observed dynamics. The mined PM model is then transformed into a QFLan model, accessible to PL engineers. Using two well-known PL models, we demonstrate the effectiveness and scalability of our methodology in pinpointing issues and suggesting fixes. Additionally, we show its generality by applying it to the security domain., Comment: Pre-print Special Issue on Managing Variability in Complex Software-Intensive Systems of the Journal of Systems and Software