A Time–Frequency Multi-Task Framework for Network Monitoring and Anomaly Detection

Abstract

Networked systems continuously generate heterogeneous time series, including Key Performance Indicator (KPI) streams, logs, and spectrum measurements, whose interpretation is essential for automated monitoring, diagnosis, and control. Existing analysis approaches either rely heavily on labeled data specific to each deployment or fail to capture joint time-domain and frequency-domain characteristics that are common in communication signals. Motivated by these limitations for cognitive communications and network monitoring, we propose the Time-Frequency Multi-Task Network (TFMTNet), a self-supervised framework that provides a transferable representation module for the evaluated network telemetry tasks. TFMTNet integrates a multi-scale time-frequency fusion backbone with three complementary pretraining objectives and provides lightweight task heads for anomaly detection, forecasting, and classification. Under a pretraining and adaptation protocol, the model is pretrained once and then adapted to target domains with limited labeled data. Empirical evaluation on ten anomaly detection datasets and additional public classification and forecasting datasets, including an Artificial Intelligence for IT Operations (AIOps) telemetry dataset, shows consistent cross-domain improvements under the evaluated settings. For cognitive communications, TFMTNet learns representations that can feed downstream reasoning and control modules, including Software-Defined Networking (SDN) decision making, spectrum management, and AIOps pipelines, thereby supporting the perception, reasoning, and control loop in networked systems.