## 5 Experiments and Evaluation
### Results and Discussion  
The CM-DANA algorithm was evaluated for anomaly detection using a comparison of baseline methods, including statistical methods, clustering-based methods, classification-based methods, and deep learning-based methods. Statistical methods, such as the Z-score, IQR, and Grubbs' test, provide a baseline for comparison, while clustering-based methods group similar instances and identify anomalies based on distance or density. Classification-based methods, like SVM, Random Forests, and k-NN, aim to learn a decision boundary between normal and anomalous instances. Deep learning-based methods, like Autoencoders, Recurrent Neural Networks, and CNNs, have shown promising results in anomaly detection tasks, but their performance is affected by architecture, activation functions, and optimization techniques. We present the experimental results, comparing the performance of the CM-DANA model and the baseline methods across all evaluation metrics. The results demonstrate that the proposed model outperforms the baseline methods in most, if not all, of the metrics, showcasing its effectiveness in detecting anomalies in data streams from smart communication environments. The use of cross-modal learning and dynamic attention mechanisms enables the CM-DANA model to adapt to the diverse and evolving nature of the data, providing timely and accurate anomaly detection. Table 2 presents a performance comparison of anomaly detection methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Anomaly Type** & **Modality** & **Description** & \\ \hline DDoS Attack & Network Traffic & Introduce sudden, high-volume traffic from multiple sources, overwhelming the network. & \\ \hline Port Scanning & Network Traffic & Simulate repeated attempts to access different ports on a target system. & \\ \hline Malware Communication & Network Traffic & Generate traffic patterns resembling communication with known malware C\&C servers. & \\ \hline Unusual Protocol Usage & Network Traffic & Inject instances of uncommon or unauthorized protocols being used in the network traffic. & \\ \hline Data Exfiltration & Network Traffic & Simulate large data transfers outside the network, indicating potential data leakage. & \\ \hline Brute Force Attacks & User Behavior & Generate multiple failed login attempts in a short time, indicating password guessing. & \\ \hline Insider Threat & User Behavior & Simulate an authorized user accessing sensitive files or systems they do not normally use. & \\ \hline Abnormal Application Usage & User Behavior & Introduce unusual sequences of application usage or accessing applications at odd times. & \\ \hline Log Tampering & Log Files & Inject altered log entries to cover up malicious activities or unauthorized access. & \\ \hline Privilege Escalation & User Behavior & Simulate a user gaining unauthorized access to higher-level privileges or systems. & \\ \hline System Resource Abuse & Log Files & Create log entries indicating excessive use of system resources or suspicious activity. & \\ \hline Time-Based Anomalies & All Modalities & Introduce events that occur at unexpected times or during unusual hours. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Anomalies injected into the synthetic dataset.