## 3 Materials and Methods  
The proposed CM-DANA consists of 4 main modules: the Feature Extraction Module, Cross-modal Learning Module, the Dynamic Attention Module, and the Anomaly Detection Module. The architecture is designed to process and analyze heterogeneous data streams from different cyber modalities, such as network traffic, log files, and user behavior patterns. The Feature Extraction Module extracts features from each modality; the Cross-modal Learning Module learns shared representations. The Dynamic Attention Module then computes attention weights to emphasize the most relevant features, forming an attention vector. Finally, the Anomaly Detection Module uses the attention vector to identify anomalous patterns. An efficient and novel combination of intelligent algorithms is used in the CM-DANA method. Specifically, it is a combination of Convolutional Neural Networks (CNNs) for feature extraction, Transformers for cross-modal learning, Gated Recurrent Units (GRUs) for dynamic attention, and Theil-Sen Regressor as an anomaly detector. This combination leverages the strengths of each algorithm to enhance predictability performance. A high-level representation of the CM-DANA methodology is presented in the following Algorithm 1:  
```
Feature Extraction Module  def feature_extraction(input_data): # Input Data Preparation  preprocessed_data = preprocess(input_data) # Convolutional Layers  convolution_output = apply_convolutional_layers(preprocessed_data) # Activation Functions  activated_output = apply_activation_functions(convolution_output) # Pooling Layers  pooled_output = apply_pooling_layers(activated_output) # Flattening  flattened_output = flatten(pooled_output) # Fully Connected Layers  features = apply_fully_connected_layers(flattened_output) return features # Cross-modal Learning Module  def cross_modal_learning(modalities):  shared_representations = []  for modality in modalities:  features = feature_extraction(modality)  shared_representations.append(features) # Process shared representations using Transformers  processed_representations = process_with_transformers(shared_representations)  return processed_representations # Dynamic Attention Module  def dynamic_attention(shared_representations):  attention_vector = []  for representation in shared_representations:  attention_weights = compute_attention_weights(representation)  attention_vector.append(weighted_sum(representation, attention_weights))  return attention_vector # Anomaly Detection Module  def anomaly_detection(attention_vector): # Use Theil_SenRegressor for linear regression  model = Theil_SenRegressor()  model.fit(attention_vector) # Calculate residuals  predicted_values = model.predict(attention_vector)  residuals = calculate_residuals(attention_vector, predicted_values) # Set dynamic threshold  threshold = set_dynamic_threshold(residuals) # Identify anomalies = identify_anomalies(residuals, threshold)  return anomalies # CM-DANA Methodology  def CM_DANA(input_modalities):
```  
**Algorithm 1** Pseudocode of CM-DANA methodology
The end-to-end training approach of the CM-DANA model ensures that the model learns to identify and capture the complex interactions between different modalities of data. This leads to more accurate anomaly detection in smart communication environments where data streams from multiple sources can provide valuable information about anomalies and potential threats. It must be noted that the 4 modules of the proposed methodology introduce significant innovative aspects that collectively enhance the accuracy and efficiency of anomaly detection in the proposed CM-DANA model. Specifically, the use of CNNs for feature extraction is an innovation that tailors the architecture to the nuances of cybersecurity data. While CNNs are commonly used for image analysis, adapting them to cybersecurity data highlights a key innovation.