John Paul Martin Encinas

arXiv2026

Federated Cybersecurity Testbed as a Service (FCTaaS): A framework to federate cybersecurity testbeds

Rapid technological change is reshaping society through emerging domains such as autonomous vehicles and smart manufacturing, creating new research challenges in system design, operation, security, and training. Researchers often rely on testbeds to reproduce experimental scenarios, collect and analyze data, observe system behavior, and evaluate proposed solutions. However, the fast pace of innovation makes it difficult and costly for individual testbeds to remain representative of state-of-the-art systems, as doing so requires frequent upgrades and new capabilities. Moreover, access to specialized testbeds is often limited to a small group of researchers, leaving valuable infrastructure underutilized during its operational lifetime. This paper presents FCTaaS, a Federated Cybersecurity Testbed as a Service framework that enables heterogeneous cybersecurity testbeds to participate in a single experiment across geographical boundaries. By connecting independently managed testbeds through a Virtual Private Network (VPN), FCTaaS supports remote testbed discovery, experiment design, and coordinated experimentation. We evaluate FCTaaS across three case studies involving denial-of-service scenarios on smart infrastructure and intrusion detection and prevention workflows using a Suricata-based IDS/IPS testbed. The results show that FCTaaS enables effective cross-testbed experimentation while preserving visibility into attack traffic, IDS alerts, and detection-system resource stress. Even under resource-intensive attack scenarios, FCTaaS achieves limiting network utilization of 49%, introduces only 1% overhead, and supports latency ranging from 5.63 ms between local nodes to 147 ms between geographically dispersed nodes.

cs.CR↗

arXiv2026

LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight

Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affect inference has become increasingly feasible, prior approaches often treat sentiment as a deterministic point estimate for individual speakers, failing to capture the inherent subjectivity, latent ambiguity, and sequential coupling found in mutual exchanges. We introduce LLM-MC-Affect, a probabilistic framework that characterizes emotion not as a static label, but as a continuous latent probability distribution defined over an affective space. By leveraging stochastic LLM decoding and Monte Carlo estimation, the methodology approximates these distributions to derive high-fidelity sentiment trajectories that explicitly quantify both central affective tendencies and perceptual ambiguity. These trajectories enable a structured analysis of interpersonal coupling through sequential cross-correlation and slope-based indicators, identifying leading or lagging influences between interlocutors. To validate the interpretive capacity of this approach, we utilize teacher-student instructional dialogues as a representative case study, where our quantitative indicators successfully distill high-level interaction insights such as effective scaffolding. This work establishes a scalable and deployable pathway for understanding interpersonal dynamics, offering a generalizable solution that extends beyond education to broader social and behavioral research.

cs.CL↗