Continuous authentication based on behavioral biometrics is effective and crucial as user behaviors are not easily copied. However, relying solely on one behavioral biometric limits the accuracy of continuous authentication. Therefore, a continuous authentication system based on multimodal behavioral biometrics fusion is proposed in this study, which fuses three modalities: contextual behavior, mouse behavior, and information interaction behavior. The multimodal dataset of user behavior is collected through a self-built website, and the behavioral feature sets for each modality are then created. An improved generative adversarial network method is used to align the datasets of the three modalities. The autoencoder with long short-term memory is employed for unsupervised anomaly detection of time-series behaviors and enables continuous authentication for each modality. The multimodal fusion is achieved using the meta-model of the stacked generalization method, and the final decision for continuous authentication is then determined. The experimental results demonstrate that the proposed multimodal fusion method significantly outperforms the unimodal and provides an effective way to improve the accuracy and user-friendliness of continuous authentication. This study offers insights into user behavior analysis, behavioral anomaly detection, and multimodal behavior fusion.

Manufacturing businesses in today's market are facing immense pressures to react rapidly to dynamic variations in demand distributions across products and changing product mixes. To cope with the pressures requires dynamically integrated manufacturing systems (DIMS) that can manage optimal fulfillment of customer orders while simultaneously considering alternative system structures to suit changing conditions. This paper presents a multiagent approach to DIMS, where production planning and control decisions are integrated with systems reconfiguration and restructure. A multiagent framework, referred to as a hierarchical autonomous agent network, is proposed to model complex manufacturing systems, their structures, and constraints. It allows the hierarchical structures of complex systems to be modeled while avoiding centralized control in classical hierarchical/hybrid frameworks. Subsystems interact heterarchically with product orders to carry out optimal planning and scheduling. An agent coordination algorithm, operating iteratively under the control of a genetic algorithm, is developed to enable optimal planning and control decisions for order fulfillment to be made through interactions between agents. This algorithm also allows the structural constraints of systems to be relaxed gradually during agent interaction, so that planning and control are first carried out under existing constraints, but when satisfactory solutions cannot be found, subsystems are allowed to regroup to form new configurations. Frequently used configurations are detected and evaluated for system restructure. The approach also enables Petri-net models of new system structures to be generated dynamically and the structures to be evaluated through agent-based discrete event simulation.