## 6 Conclusions and Future Work  
A CM-DANA was proposed in the paper, a novel and promising approach for detecting anomalies in data streams from smart communication environments. The model extends the state-of-the-art attention mechanism by using a hybrid method called cross  
Figure 8: Cumulative Gains Curve Plot of CM-DANA. Figure 9: Kolmogorov–Smirnov (KS) Statistic Plot of CM-DANA. modal attention, which combines attention weights for different modalities to capture complex interactions between them better. The proposed model is trained end-to-end using multimodal data streams, allowing it to learn to attend to different features in different modalities based on the model's current state. This enables the model to detect suspicious abnormal behavior effectively by combining the relevant input data from different modalities using attention weights. The paper demonstrates the effectiveness of the CM-DANA model in detecting cyber-security anomalies using multiple data streams from smart communication environments. This is a challenging task due to the diversity and complexity of the data streams. Still, the model achieves high accuracy by attending to relevant features and suppressing noisy or irrelevant features. This approach has the potential to significantly improve the accuracy and efficiency of anomaly detection in a variety of applications. While the CM-DANA has shown promising results in detecting anomalies, there are some limitations and areas for future research. Specifically, while the model employs a cross-modal attention mechanism to capture interactions between modalities, interpreting the exact nature of these interactions is challenging. Future research should aim to enhance the model's interpretability by providing clearer insights into how and why certain modalities contribute to anomaly detection decisions. Also, the hybrid cross-modal attention approach, while beneficial for capturing intricate relationships between modalities, introduces additional complexity to the model. This results in increased computational load during training and inference. Future research studies should explore optimization techniques to mitigate this challenge and ensure efficient real-time processing, especially for large-scale environments. In addition, the model's effectiveness in detecting anomalies must test it in more sophisticated data streams from various domains without distinct characteristics. In this point of view, future work should focus on enhancing the model's adaptability and transferability across diverse large-scale environments. Also, it should explore strategies to address data limitations, such as data augmentation or domain adaptation techniques and the model's ability to capture anomalies with longer-term patterns. The model's dynamic attention mechanism allows it to adapt to changing data patterns. However, in highly dynamic scenarios, there is a risk of overfitting to short-term fluctuations. Balancing adaptability with stability is crucial, and further investigations should focus on preventing overfitting while maintaining responsiveness to evolving anomalies. Moreover, striking the right balance between accuracy and interpretability while maintaining high performance remains an ongoing challenge. Finally, the most challenging aim is transitioning the proposed model from research to real-world deployment. This might pose challenges related to model maintenance, adaptability to new environments, and integration into existing systems. Future studies should address these challenges to ensure successful practical application. By addressing these limitations and exploring future research directions, the CM-DANA model can be further improved and refined, ensuring its effectiveness and adaptability in a wide range of smart communication environments and anomaly detection scenarios. Conceptualization, K.D. and K.R. ; methodology, K.D. ; software, K.D. ; validation, K.D., K.R., L.M. and L.I. ; formal analysis, K.D. and K.R. ; investigation, K.D. and K.R. ; resources, L.M.