Abstract Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose BioEnvSense, a context-aware human-centred security framework that fuses soft biometric and environmental sensing to estimate user’s physiological and cognitive states in real time. At the core of the framework is a subject-independent risk inference engine, implemented as a hybrid CNN-LSTM model, selected for its suitability for low-latency IoT edge deployment. The CNN component extracts spatial patterns from multimodal sensor data, while the LSTM captures the temporal dynamics of human error susceptibility. The model achieves 84% accuracy, demonstrating the feasibility of detecting proxy states of physiological and environmental conditions associated with elevated cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework provides a foundation for proactive interventions; empirical validation against real-world incident data remains a primary direction for future work.

Abstract The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.