UB-NET: multimodal IoT-based user authentication using unified multi-model behavioural embedding with custom deep neural network

The recognition of user behavioural patterns provides an innovative method for access control systems, as it relies on automatic detection of human behaviour and does not require user involvement or the recall of credentials. These actions can be captured surreptitiously by passive sensors placed within the user’s environment. Existing solutions fail to integrate real-time physiological and behavioural patterns for robust user verification. This work presents an advanced multimodal user behavioural authentication framework that incorporates data from electroencephalogram (EEG) signals, Internet of Things (IoT) signals, radar point clouds, and cognitive workload metrics National Aeronautics and Space Administration Task Load Index (NASA-TLX) to capture a user’s unique interaction signature. A specialised feature-extraction pipeline called unified multi-model behavioural embedding (U-MoBE) converts raw sensor data into statistically and semantically rich representations using spectral, entropy-based, temporal, and spatial descriptors. These features are processed by a custom deep neural network for user behaviour analysis (UserBehaviorNet, UB-NET), which supports both closed-set (unknown) and open-set (registered) user identification via a confidence-based rejection method. The model achieves 100% accuracy on both the training and test sets, with minimal cross-entropy losses of 0.0203 and 0.0167, respectively. A confusion matrix assures perfect classification, while the SHapley Additive exPlanations (SHAP) analysis explains the impact of NASA-TLX components on model decisions. Additionally, inference validation on unknown users exhibits strong generalisation and secure rejection, establishing the framework’s efficacy in real-time authentication scenarios.