BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents
This addresses cybersecurity for organizations by enabling proactive interventions to reduce human-driven incidents, though it appears incremental as it builds on existing neural network methods.
The paper tackled the problem of cybersecurity incidents caused by human behavior by proposing a conceptual security framework that integrates a hybrid CNN-LSTM model to analyze biometric and environmental data, achieving 84% accuracy in detecting conditions that lead to elevated human-centred cyber risk.
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