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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.19410 |
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| _version_ | 1866914343548878848 |
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| author | Ta, Duy Anh Farid, Farnaz Ahamed, Farhad Al-Areqi, Ala Beutel, Robert Watson, Tamara Maurushat, Alana |
| author_facet | Ta, Duy Anh Farid, Farnaz Ahamed, Farhad Al-Areqi, Ala Beutel, Robert Watson, Tamara Maurushat, Alana |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19410 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents Ta, Duy Anh Farid, Farnaz Ahamed, Farhad Al-Areqi, Ala Beutel, Robert Watson, Tamara Maurushat, Alana Cryptography and Security Computers and Society Human-Computer Interaction Machine Learning F.2.2; I.2.7 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 |
| title | BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents |
| topic | Cryptography and Security Computers and Society Human-Computer Interaction Machine Learning F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2602.19410 |