Enhancing Threat Model Validation: A White-Box Approach based on Statistical Model Checking and Process Mining

Our method addresses the challenge of validating threat models by comparing actual behavior with expected behavior. Statistical Model Checking (SMC) is frequently the more appropriate technique for validating models, as it relies on statistically relevant samples to analyze systems with potentially infinite state spaces. In the case of black-box systems, where it is not possible to make complete assumptions about the transition structure, black-box SMC becomes necessary. However, the numeric results of the SMC analysis lack insights on the model’s dynamics, prompting our proposal to enhance SMC analysis by incorporating visual information on the behavior that led to a given estimation. Our method improves traditional model validation using SMC by enriching its analyses with Process Mining (PM) techniques. Our approach takes simulated event logs as inputs, and uses PM techniques to reconstruct an observed model to be compared with the graphical representation of the original model, obtaining a diff model highlighting discrepancies among expected and actual behavior. This allows the modeler to address unexpected or missing behaviors. In this paper we further customize the diff model for aspects specific to threat model analysis, incorporating features such as new colored edges to symbolize an attacker’s initial assets and a automatic fix for simple classes of modeling errors which generate unexpected deadlocks in the simulated model. Our approach offers an effective and scalable solution for threat model validation, contributing to the evolving landscape of risk modeling and analysis

Consolidative thoracic radiation therapy for extensive-stage small cell lung cancer in the era of first-line chemoimmunotherapy: preclinical data and a retrospective study in Southern Italy

Background: Consolidative thoracic radiotherapy (TRT) has been commonly used in the management of extensive-stage small cell lung cancer (ES-SCLC). Nevertheless, phase III trials exploring first-line chemoimmunotherapy have excluded this treatment approach. However, there is a strong biological rationale to support the use of radiotherapy (RT) as a boost to sustain anti-tumor immune responses. Currently, the benefit of TRT after chemoimmunotherapy remains unclear. The present report describes the real-world experiences of 120 patients with ES-SCLC treated with different chemoimmunotherapy combinations. Preclinical data supporting the hypothesis of anti-tumor immune responses induced by RT are also presented. Methods: A total of 120 ES-SCLC patients treated with chemoimmunotherapy since 2019 in the South of Italy were retrospectively analyzed. None of the patients included in the analysis experienced disease progression after undergoing first-line chemoimmunotherapy. Of these, 59 patients underwent TRT after a multidisciplinary decision by the treatment team. Patient characteristics, chemoimmunotherapy schedule, and timing of TRT onset were assessed. Safety served as the primary endpoint, while efficacy measured in terms of overall survival (OS) and progression-free survival (PFS) was used as the secondary endpoint. Immune pathway activation induced by RT in SCLC cells was explored to investigate the biological rationale for combining RT and immunotherapy. Results: Preclinical data supported the activation of innate immune pathways, including the STimulator of INterferon pathway (STING), gamma-interferon-inducible protein (IFI-16), and mitochondrial antiviral-signaling protein (MAVS) related to DNA and RNA release. Clinical data showed that TRT was associated with a good safety profile. Of the 59 patients treated with TRT, only 10% experienced radiation toxicity, while no ≥ G3 radiation-induced adverse events occurred. The median time for TRT onset after cycles of chemoimmunotherapy was 62 days. Total radiation dose and fraction dose of TRT include from 30 Gy in 10 fractions, up to definitive dose in selected patients. Consolidative TRT was associated with a significantly longer PFS than systemic therapy alone (one-year PFS of 61% vs. 31%, p<0.001), with a trend toward improved OS (one-year OS of 80% vs. 61%, p=0.027). Conclusion: Multi-center data from establishments in the South of Italy provide a general confidence in using TRT as a consolidative strategy after chemoimmunotherapy. Considering the limits of a restrospective analysis, these preliminary results support the feasibility of the approach and encourage a prospective evaluation

Process Mining Meets Statistical Model Checking: Towards a Novel Approach to Model Validation and Enhancement

We propose a novel research line integrating Statistical Model Checking (SMC), a family of simulation-based analysis techniques from quantitative formal methods, with Process Mining (PM), a collection of data-driven process-oriented techniques. SMC and PM are complementary. SMC focuses on performing the right number of simulations to obtain statistically-reliable estimations (e.g., the probability of success of an attack). PM focuses on reconstructing a model of a system using logs of its traces. Nevertheless, both approaches aim at providing evidence of issues/guarantees of the system, and at proposing enhancements. We aim at enriching SMC by explaining why it produced specific estimates. This might help, e.g., identifying issues in the model (validation) or suggesting improvements (enhancement). Given that SMC uses statistics to decide what is the correct number of simulations (or traces), we avoid by-construction the complex issue of under-representation of system behavior in the logs crucial to many PM exercises. This work-in-progress paper demonstrates the proposed methodology and its usefulness using a simple example from the security threat modeling domain. We show how PM helps highlighting both mistakes in the model, and possibilities for improvement