![[2602.19410] BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19410] BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents
Summary
The paper introduces BioEnvSense, a human-centered security framework that leverages a hybrid CNN-LSTM model to analyze biometric and environmental data, aiming to prevent behavior-driven cyber incidents with 84% accuracy.
Why It Matters
As organizations increasingly face cyber threats stemming from human behavior, this framework addresses a critical gap in cybersecurity by integrating advanced machine learning techniques to enhance security measures. Its focus on continuous monitoring and adaptive safeguards could significantly reduce human error-related incidents.
Key Takeaways
- BioEnvSense integrates CNN and LSTM models for enhanced security.
- The framework analyzes biometric and environmental data for context-aware decisions.
- Achieves 84% accuracy in detecting conditions leading to cyber risks.
- Supports proactive interventions to mitigate human-driven cyber incidents.
- Focuses on human behavior as a key factor in cybersecurity.
Computer Science > Cryptography and Security arXiv:2602.19410 (cs) [Submitted on 23 Feb 2026] Title:BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents Authors:Duy Anh Ta, Farnaz Farid, Farhad Ahamed, Ala Al-Areqi, Robert Beutel, Tamara Watson, Alana Maurushat View a PDF of the paper titled BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents, by Duy Anh Ta and 6 other authors View PDF 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 Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) ACM classes: F.2.2; I.2.7 Cite as: arXiv:2602.19410 [cs.CR] (or arXiv:2602.1...
