Self-Supervised Spatial-Temporal Representation Learning for Autonomous Network Traffic Prediction and Anomaly Detection
Keywords:
self-supervised learning, spatial-temporal representation, network traffic prediction, anomaly detection, autonomous networking, system architecture, governanceAbstract
The accelerating growth of heterogeneous network infrastructures, including 5G, edge-cloud ecosystems, and large-scale data center interconnects, demands new paradigms for autonomous traffic management. Traditional supervised approaches to network traffic prediction and anomaly detection suffer from extensive labeling costs, limited generalization across dynamic environments, and brittle responses to novel traffic patterns. Self-supervised spatial-temporal representation learning has recently emerged as a transformative framework to address these challenges by extracting rich, transferable features from unlabeled traffic data. This paper provides a system-level examination of the design, deployment, and governance implications of such learning paradigms within autonomous networking systems. We analyze the architectural trade-offs inherent in building pipelines that integrate self-supervised pre-training with downstream prediction and detection tasks, emphasizing modularity, scalability, and real-time responsiveness. By comparing contrastive, predictive, and masked reconstruction objectives for spatial-temporal network data, we highlight structural decisions that affect robustness to traffic regime shifts and adversarial perturbations. The discussion extends to infrastructure considerations, including the orchestration of streaming data flows, the embedding of inference engines at the network edge, and energy-aware resource management. Crucially, the paper addresses the socio-technical dimensions of autonomous network intelligence, examining fairness in automated anomaly response, transparency of learned representations, and alignment with evolving regulatory frameworks. By synthesizing cross-domain insights from systems engineering, machine learning, and policy studies, we articulate a comprehensive research agenda for responsible and resilient autonomous network operations. The analysis underscores that the full potential of self-supervised learning in this domain can only be realized through a holistic integration of algorithmic innovation, principled system design, and attentive governance.
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