## 3 Materials and Methods
### Feature Extraction Module  
The feature extraction module is responsible for processing the input data from different modalities and extracting relevant features that capture the characteristics of the data. It plays a crucial role in representing the data in a format that the subsequent modules can effectively analyze. The features extracted from each subnetwork are then passed through a fusion layer, which learns to combine the multimodal features into a single shared representation. This representation is used as the input for the subsequent cross-modal learning module. It must be noted that the input processing layer of the features extraction module takes in data streams from multiple modalities, such as data acquisition systems, sensors, or web services. CNNs are particularly effective at extracting spatial features from input data, making them suitable for processing certain modalities. Specifically, CNNs architecture (Figure 1) have shown excellent performance in extracting spatial features from data, making them suitable for processing data streams from multiple cybersecurity modalities. The integration of Convolutional Neural Networks (CNNs) for the purpose of feature extraction within the CM-DANA architecture involves a series of sequential procedures. Specifically, commencing with Step 1, the preparation of input data is undertaken. Data originating from diverse modalities is subjected to preprocessing procedures to conform to formats conducive to CNN-compatible representations. In Step 2, the architecture employs a succession of convolutional layers to process the input data. Within these layers, convolutions are executed using adaptable filters, which effectively capture spatial features across varying levels of abstraction. The parameter adaptability, encompassing filter depth and size, assumes significance in ensuring proficient feature extraction that corresponds to the intricacy inherent in the data. Following each convolutional layer, as elucidated in Step 3, non-linear activation functions, such as the Rectified Linear Unit (ReLU), are introduced. This introduction of non-linearity serves the purpose of capturing intricate patterns present within the data. Strategic insertion of pooling layers, as delineated in Step 4, contributes to the overall architecture. These pooling layers, which encompass MaxPooling and AveragePooling, serve the dual role of diminishing computational complexity and preserving pertinent features. The outcome of these layers is a downsampling of feature maps, thereby fostering spatial invariance. Step 5 entails the flattening of output feature maps that are generated by the convolutional layers. This flattening operation transforms the feature maps into one-dimensional vectors, thereby preparing them for subsequent stages of processing. Transitioning to Step 6, the flattened features are directed into fully connected layers. The role of these layers is to enhance the extracted features by capturing more complex relationships and representations that exist at higher levels of abstraction. Finally, Step 7 culminates in the  
Figure 1: A Convolutional Neural Network (CNN). generation of a distinct output. The output stems from the fully connected layers and serves as a unique representation of features. This representation, in essence, encapsulates crucial spatial information inherent within the input data. The innovation of CM-DANA becomes evident in its incorporation of CNNs tailored for anomaly detection across smart communication environments. Specifically, the proposed approach introduces a pioneering innovation that lies in the thoughtful integration of CNNs module for feature extraction, specifically designed to address the challenges of cybersecurity modalities by extracting spatial features that hold particular significance in cybersecurity contexts. Unlike conventional anomaly detection approaches, which often employ generic feature extractors, CM-DANA tailors its feature extraction to the nuances of the data, enhancing its anomaly detection prowess.