arXiv Machine Learning
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
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arXiv AI
Jun 9
XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection
arXiv:2502. 09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance.
arXiv Machine Learning
Jul 7
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
arXiv AI
Aug 26
Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification
arXiv:2603.25507v2 Announce Type: replace-cross Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
arXiv Machine Learning
Sep 16
Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification
The paper proposes a human-centered framework for validating the semantic soundness of machine learning models used in network traffic classification. It extends existing knowledge-generation methods by integrating data, models, explainability tools, visualizations, and expert reasoning to iteratively explore, verify, and refine model behavior and preprocessing steps. The framework is built on literature findings, benchmark analyses, XAI experience, and expert feedback, offering practical guidance for ensuring models learn trustworthy, semantically meaningful patterns rather than spurious correlations.
arXiv Machine Learning
Sep 11
A Dynamic Fusion Large Language Model for Traffic Flow Prediction
The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets demonstrate that DF-LLM outperforms existing methods in predictive accuracy.
arXiv AI
Aug 17
Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
arXiv:2608. 13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications.