mlsanityData to 14 Oct 2026

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

arXiv 2505.08220 ↗First posted 13 May 2025cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 13 May 2025.

Builds on

12 of 26 references.

References · 12

  1. 2019TENER: Adapting Transformer Encoder for Named Entity Recognitioncited by 69
  2. 2025Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimizationcited by 10
  3. 2025Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaborationcited by 9
  4. 2025Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inferencecited by 9
  5. 2025Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Modelscited by 8
  6. 2024Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equationscited by 7
  7. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  8. 2025Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Miningcited by 6
  9. 2025Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQNcited by 6
  10. 2025A Deep Learning Approach to Interface Color Quality Assessment in HCIcited by 5
  11. 2025State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordinationcited by 5
  12. 2025Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuningcited by 5

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Cited by year

Citing papers per year: 2025: 6.

6 papers.

  1. 2025Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Servicescited by 9
  2. 2025Structure-Aware Temporal Modeling for Chronic Disease Progression Predictioncited by 7
  3. 2025Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessmentcited by 3
  4. 2025Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modelingcited by 1
  5. 2025Contextual Learning for Anomaly Detection in Tabular Datacited by 1
  6. 2025Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networkscited by 0