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

TL;DR

BioEnvSense introduces a human-centric cybersecurity framework using a CNN-LSTM model to analyze biometric and environmental data, enabling proactive detection and prevention of human-driven cyber incidents.

Contribution

It presents a novel hybrid CNN-LSTM model integrated into a security framework for real-time, context-aware detection of human error risks in cybersecurity.

Findings

Achieved 84% accuracy in detecting high-risk human conditions.

Demonstrated effective continuous monitoring for proactive security.

Enabled adaptive safeguards to reduce human-driven cyber incidents.

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