The accurate prediction of Quality of Experience (QoE) in video streaming services is essential for optimizing user satisfaction and network performance. While traditional Quality of Service (QoS) metrics provide objective measurements of network behavior, they often fail to reflect the subjective nature of user experience. This paper investigates the use of Machine Learning models to estimate QoE based on QoS indicators. Building upon the recently published SNESet dataset, we evaluate a range of modern regression techniques, including randomization-based neural networks, symbolic regression and Kolmogorov-Arnold Networks, alongside other traditional and ensemble-based models. A central focus of this study is the explainability of such new models, which enables the extraction of domain-relevant insights from the learned relationships. Using model-agnostic techniques for explainable Artificial Intelligence and uncertainty quantification, we assess the confidence of such models in their predictions and analyze the contribution of individual features to the estimated QoE. Our results underscore the need for explainable QoE prediction systems, closing the gap between data-driven modeling and domain expertise.
A Matching model describes the waiting times suffered by items before they match with other items and disappear immediately without service. The matching relation is described by a compatibility graph. It is an easy representation of multiple types synchronizations between items. When we add an edge in the compatibility graph, one expects that the expected total number of items decreases. Unexpectedly this is not always true and there is a performance paradox. Extending previous results, we show new matching models with performance paradoxes which seems to be more frequent than one can expect.
In cybersecurity, moving-target defense (MTD) mitigates reconnaissance attacks from botnets by periodically resetting server identities, disrupting adversarial intelligence gathering. We model this defense mechanism as a dynamic coupon collection process, where servers correspond to coupon types and botnet attacks to coupon acquisitions. The defender’s intervention (removing collected coupons) represents server resets, restoring their uncompromised state. In this paper, we derive various results regarding the number of compromised servers under various stochastic attack-defense dynamics.
Modern cyberattacks increasingly involve coordinated teams of adversaries, posing new challenges for detection and defense. This paper introduces a probabilistic framework for modeling such multi-agent attacks, combining Markov Decision Processes with an augmented Markov game that captures dynamic team composition under detection events. The model integrates state-dependent detection probabilities, implicit coordination via reward structures, and efficient value iteration for policy computation. Through a stochastic grid-world case study, we analyze the performance trade-offs between isolated and coordinated strategies. Our results reveal that while isolated agents perform better in simple, single-target scenarios, coordinated strategies significantly improve success rates in complex, multi-target operations by systematically managing exposure. This work provides a formal foundation and practical methodology for analyzing emergent attack behaviors in adversarial environments.
This paper addresses decision-making problems where the objective is to maximize the best outcome along a trajectory in a Markov Decision Process (MDP), rather than its cumulative reward. Such extremal objectives naturally arise in risk-sensitive applications, including cybersecurity and resilience planning, but fall outside the scope of classical MDP theory due to their non-Markovian nature. We propose a principled transformation that augments the MDP state space with a deterministic memory variable tracking the maximal reward, yielding an equivalent total-reward MDP solvable by dynamic programming. This construction enables the extraction of Markovian policies through a provision function tailored to each initial state. We evaluate our framework on a malware containment problem, showing that the memory-based baseline policy significantly outperforms a greedy myopic strategy across all configurations. Our results demonstrate improved worst-case cost and containment time, highlighting the utility of memory-augmented planning for extremal performance control in stochastic systems.
