AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
IA-AD introduces a one-dimensional Inception-Attention encoder that combines multi-scale temporal convolutions with lightweight attention to capture short-term fluctuations and longer-range variations in multivariate time series anomaly detection.
CARE, a model-agnostic cascaded inference framework that integrates a Lightweight Pre-filter Model (LPM) with an existing high-capacity Complex Detection Model (CDM) and introduces a Structure Attention module to explicitly capture channel-wise anomaly contributions, achieves inference speedup compared to the most accu...
Multivariate time-series anomaly detection is central to modern cyber-physical and cloud monitoring systems. While most detectors rely solely on sensor or telemetry streams, operational anomalies are often accompanied by textual evidence such as logs. This paper presents a conditional multimodal framework for time-seri...
Robust anomaly detection in time series remains challenging because sparse abnormal observations, noise contamination, nonlinear dynamics, and long-range temporal dependencies can obscure deviation patterns. This paper proposes the SALK anomaly detection model, which integrates an attention-enhanced long short-term mem...
DAMR, a novel dual adaptive multi-head representation learning framework for the MTSAD task that achieves significant performance improvements and strong noise robustness and requires far less FLOPs and GPU memory cost than Transformer-based temporal modeling works.
Transformer with Variational AutoEncoder is presented, a framework that combines a dual-embedding representation pipeline, a Transformer encoder for long-range dependency modeling, and VAE-based latent regularization of encoded features to provide an effective and robust solution for long-term time-series anomaly detec...
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 dep...
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