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

2026-02-22

AI Summary

This study addresses cybersecurity risks stemming from human behavior by proposing a human-centric security framework that, for the first time, integrates biosignal and environmental contextual data to enable context-aware, real-time detection of high-risk human actions. The framework employs a hybrid CNN-LSTM deep learning model, where the convolutional neural network (CNN) extracts spatial features from multi-source sensor data, and the long short-term memory (LSTM) network captures temporal dynamics indicative of error-prone states. Experimental results demonstrate an accuracy of 84% in identifying human conditions likely to precipitate security incidents. The approach supports continuous monitoring and adaptive intervention, thereby establishing a novel paradigm for proactive, human-centered cyber defense.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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

Problem

• human-centred security
• cyber incidents
• behaviour-driven risk
• biometric data
• context-aware security

Innovation

• CNN-LSTM hybrid model
• behavior-driven cybersecurity
• biometric-environmental sensing
• context-aware security
• human error susceptibility