5.1 Tensor-Based Anomaly Detection

Utilizing multi-layer convolutional recurrent neural networks (CRNNs) with attention mechanisms, the ADS processes high-dimensional time-series data streams to detect anomalous patterns.

Model Architecture:

  • Input Tensor Shape: (Batch, Channels, Timesteps, Features)

  • Convolutional Layers: Extract localized feature embeddings from multivariate sequences.

  • Bidirectional LSTM Stack: Capture long-range temporal dependencies bidirectionally.

  • Self-Attention Layer: Weight sequence elements by contextual relevance to anomaly likelihood.

  • Output Softmax Layer: Classify sequences into normal and anomalous categories.

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