PHASE: Passive Human Activity Simulation Evaluation

Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE (Passive Human Activity Simulation Evaluation), a machine learning framework that analyzes Zeek connection logs and distinguishes human from non-human activity with over 90\% accuracy. PHASE operates entirely passively, relying on standard network monitoring without any user-side instrumentation or visible signs of surveillance. All network activity used for machine learning is collected via a Zeek network appliance to avoid introducing unnecessary network traffic or artifacts that could disrupt the fidelity of the simulation environment. The paper also proposes a novel labeling approach that utilizes local DNS records to classify network traffic, thereby enabling machine learning analysis. Furthermore, we apply SHAP (SHapley Additive exPlanations) analysis to uncover temporal and behavioral signatures indicative of genuine human users. In a case study, we evaluate a synthetic user persona and identify distinct non-human patterns that undermine behavioral realism. Based on these insights, we develop a revised behavioral configuration that significantly improves the human-likeness of synthetic activity yielding a more realistic and effective synthetic user persona.

CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams

Early identification of high risk heart failure (HF) patients is key to timely allocation of life-saving therapies. Hemodynamic assessments can facilitate risk stratification and enhance understanding of HF trajectories. However, risk assessment for HF is a complex, multi-faceted decision-making process that can be challenging. Previous risk models for HF do not integrate invasive hemodynamics or support missing data, and use statistical methods prone to bias or machine learning methods that are not interpretable. To address these limitations, this paper presents CARNA, a hemodynamic risk stratification and phenotyping framework for advanced HF that takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs). This interpretable framework learns risk scores that predict the probability of patient outcomes, and outputs descriptive patient phenotypes (sets of features and thresholds) that characterize each predicted risk score. CARNA incorporates invasive hemodynamics and can make predictions on missing data. The CARNA models were trained and validated using a total of five advanced HF patient cohorts collected from previous trials, and compared with six established HF risk scores and three traditional ML risk models. CARNA provides robust risk stratification, outperforming all previous benchmarks. Although focused on advanced HF, the CARNA framework is general purpose and can be used to learn risk stratifications for other diseases and medical applications.