## 2 Literature Review
However, the application of cross-modal learning to anomaly detection in data streams from smart communication environments is still relatively unexplored. This approach offers several benefits, but also presents challenges, such as developing effective fusion strategies, addressing domain-specific issues, dealing with varying data modalities, and managing computational complexity [36]. Additionally, data privacy and ethics are critical concerns in smart communication environments, and researchers must address these concerns when designing cross-modal anomaly detection systems [25]. Attention mechanisms have been introduced in neural networks to help the model focus on the most relevant parts of the input data for a specific task [12]. The concept of attention was initially proposed in the context of Natural Language Processing (NLP) [15] and has since been extended to various domains, such as computer vision [14] and speech recognition [40]. Different types of attention mechanisms have been proposed, including self-attention [41], local attention [42], and global attention [43]. Attention mechanisms have also been combined with other neural network architectures, such as RNNs [44],CNNs [45], and Transformer models [46], to improve their performance and interpretability. The application of attention mechanisms in anomaly detection has shown promising results, particularly in terms of handling large-scale and high-dimensional data [27]. However, incorporating dynamic attention mechanisms into cross-modal learning for anomaly detection in data streams remains a challenge. Specifically, incorporating dynamic attention mechanisms into cross-modal learning for anomaly detection in data streams requires a careful balance between adaptability, efficiency, interpretability, and performance [37]. Researchers need to devise novel approaches that address these challenges and tailor dynamic attention mechanisms to the specific requirements of dynamic data streams and multi-modal data fusion [14]. Despite the challenges, successfully implementing dynamic attention can significantly enhance the accuracy and robustness of anomaly detection systems in complex and rapidly evolving environments [12]. In summary, research gaps from the literature review in anomaly detection in dynamic environments include adapting traditional methods to handle changing distributions and patterns, integrating heterogeneous data types, improving the interpretability of deep models, exploring cross-modal anomaly detection, incorporating dynamic attention mechanisms, and addressing privacy and ethics concerns. These areas highlight opportunities for innovation and exploration in anomaly detection in smart communication environments, particularly in integrating heterogeneous data types, enhancing interpretability, and effectively utilizing dynamic attention mechanisms and cross-modal learning techniques. By addressing these gaps, the proposed approach proposes a more effective anomaly detection method that can handle diverse data types, improve interpretability, and maintain privacy and ethics in cross-modal anomaly detection systems. Specifically, this paper presents a novel CM-DANA for detecting anomalies in data streams generated from smart communication environments. The proposed architecture leverages the advantages of cross-modal learning and dynamic attention mechanisms to effectively analyze heterogeneous data streams from different cyber modalities and identify anomalous patterns in real-time. Recent advancements inspire this approach in cross-modal learning and attention mechanisms in neural networks. Cross-modal learning has shown its potential in various applications where data comes from multiple sources or modalities, while attention mechanisms have been successful in helping models focus on relevant parts of input data for specific tasks.