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

arXiv: https://arxiv.org/abs/2602.19410 · HTML: https://mlsanity.com/paper/2602.19410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 57 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning](https://mlsanity.com/paper/2205.11854/) (2022)
- [Content, Nudges and Incentives: A Study on the Effectiveness and Perception of Embedded Phishing Training](https://mlsanity.com/paper/2409.01378/) (2024)

## Abstract (arXiv)

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