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arXiv · 2602.19410

BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents

Duy Anh Ta·Farnaz Farid·Farhad Ahamed·Ala Al-Areqi·Robert Beutel·Tamara Watson·Alana Maurushat
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Abstract

Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose a conceptual security framework that integrates a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze biometric and environmental data for context-aware security decisions. The CNN extracts spatial patterns from sensor data, while the LSTM captures temporal dynamics associated with human error susceptibility. The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework supports proactive interventions that reduce the likelihood of human-driven cyber incidents

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Duy Anh Ta, Farnaz Farid, Farhad Ahamed, Ala Al-Areqi, Robert Beutel, Tamara Watson, Alana Maurushat. 2026-02-23. BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents. https://arxiv.org/abs/2602.19410

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