: The cybersecurity landscape has evolved from being predominantly technical to becoming a complex socio-technical challenge, where the human element stands as a major attack vector. Traditional Security Education, Training, and Awareness programs often fail due to generalized approaches that neglect individual learner variability, the psychological costs of compliance, and the accelerating volatility of the threat landscape driven by Generative Artificial Intelligence. This work follows a Design Science Research approach, focusing on artifact design and theoretical integration for the proposal of an innovative learning system. By synthesizing topics such as the theory of Nonlinear Dynamic Motivation and the Social Engineering Attack Framework, we present a systematic multi-agent assisted architectural model for cybersecurity awareness training. We acknowledge the important role of Diegetic Connectivity in enhancing training engagement, as it seamlessly embeds learning within a realistic narrative context. As such, we propose leveraging organizational intranet data to derive more convincing and contextually grounded scenarios while reflecting on inherent data privacy concerns.
Luís Gomes, L. Batista, António Deus et al.· International Conference on...· 0 citations
: Understanding and predicting throughput time in multi-line manufacturing environments is a core challenge in industrial simulation and production planning. This paper proposes a simulation-informed analytical framework applied to a real-world event-log dataset comprising 28,026 parts produced across 13 heterogeneous lines over 13 operating days. The framework addresses three objectives: i) characterising per-line throughput distributions, ii) quantifying the impact of equipment downtime on cycle time, and iii) forecasting shift-level production using pre-shift features. Downtime is significantly associated with increased cycle times ( p = 0 . 020), although correlation patterns vary across lines. Change-point detection (PELT) identifies intra-shift disruptions in 11.2% of shifts, typically occurring in the second half, suggesting cumulative degradation effects. A consistent time-of-day effect is observed across most lines. For prediction, global models outperform per-line approaches due to data sparsity. Under a rolling-window protocol, Ridge Regression achieves R 2 = 0 . 570 (MAE = 36 . 3 parts/shift). Feature importance analysis indicates that recent production history dominates predictive performance.
J. Almeida, Raquel Paradinha, L. Afonso et al.· International Conference on...· 0 citations
A compliance management platform that operationalizes regulatory requirements through structured, expert-guided control implementation, that combines NLP extraction with human-supervised annotation to convert regulatory texts into machine-readable frameworks, enabling multi-framework management, control mapping, evidence tracking, and role-based audit workflows is presented.
Mariana Andrade, João Rafael Almeida, J. Oliveira· International Conference on...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
