## 5 Use Case of 6G-OPENSEC-Security
### Applying ML/DL and Security Policies
#### 5.3.1 Security Data Analytics  
The SDA component is the analytical powerhouse of this subdomain of components in the closed-loop system. It processes raw security data collected by the Security Data Collection component, turning them into meaningful insights that can be acted upon. Its primary focus is on dissecting and understanding the vast amounts of data expected to flow through the network and system to identify patterns, anomalies, and potential threats. In other words, the Security Data Analytics component is responsible for making sense of the massive amounts of security data constantly being collected and identifying critical threats and vulnerabilities. This component uses a variety of advanced analytics techniques, such as ML, to identify trends and anomalies in the data. Also, it generates alerts when it detects suspicious activity and can provide reports and recommendations on the overall security posture of the network. _Key Features/Responsibilities:_  
* **Proactive Analysis**: Security Data Analytics can spot possible dangers before they result in damage by examining network data. This can be achieved by detecting patterns and trends in the data, which can be indicators of consistent threats or system behaviours that need to be kept in check (such as a system vulnerability). ML algorithms can be used to help detect complex patterns. * **Anomaly Detection**: Beyond recognizing patterns, the Security Data Analytics component is responsible for detecting anomalies--unusual behaviours or data points that deviate from the norm and can indicate potential security threats. This may include any actions such as unauthorised access, suspicious network traffic, malicious user activity, and more. ML methods will be employed to find anomalies. * **ML Models Ensemble:** Security Data Analytics may also offer an ensemble approach for detecting and predicting anomalies. This approach combines the use of multiple ML models to enhance the system's robustness and accuracy. An approach could be to use anomaly detection models to separate data and then apply different types of predictors. * **Data Visualization**: To aid operators and other system components of the system in understanding the security landscape, Security Data Analytics might offer visual representations of data, highlighting key insights, threats, and patterns in real time. This would speed up any particular action that is required to be performed manually in the system. * **Security Report Generation**: It is important to create security reports regularly, to document, communicate, and analyse the systems' security state. The SDA may generate some security reports. These reports can become important documentation for legal purposes. They can also provide detailed information about security incidents, giving information about causes and impacts. Table 3 gives a detailed description of all the modules that constitute the Security Data Analytics component that work together to achieve its functionality. Figure 9 also shows the interactions between these modules by presenting the SDA workflow, detailing data transfer from SPDS to DPTE, processing by ADE and RASP, and insights sent to RM for reporting and alerts managed by AM, with periodic model testing for accuracy checks.