Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks

To improve the identification of potential anomaly patterns in complex user behavior, this paper proposes an anomaly detection method based on a deep mixture density network. The method constructs a Gaussian mixture model parameterized by a neural network, enabling conditional probability modeling of user behavior. It effectively captures the multimodal distribution characteristics commonly present in behavioral data. Unlike traditional classifiers that rely on fixed thresholds or a single decision boundary, this approach defines an anomaly scoring function based on probability density using negative log-likelihood. This significantly enhances the model's ability to detect rare and unstructured behaviors. Experiments are conducted on the real-world network user dataset UNSW-NB15. A series of performance comparisons and stability validation experiments are designed. These cover multiple evaluation aspects, including Accuracy, F1- score, AUC, and loss fluctuation. The results show that the proposed method outperforms several advanced neural network architectures in both performance and training stability. This study provides a more expressive and discriminative solution for user behavior modeling and anomaly detection. It strongly promotes the application of deep probabilistic modeling techniques in the fields of network security and intelligent risk control.