# 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.