## 7 Results and Analysis
### Security Metrics  
In this section, we evaluate the security effectiveness of our Zero Trust Architecture (ZTA) framework through various metrics, including attack success rate, resilience to malicious participants, access control enhancement, and anomaly detection performance. The results demonstrate the robustness of our approach compared to existing solutions, such as Role-Based Access Control (RBAC) and no-defense mechanisms. The security analysis (Figure 13) reveals several innovations that demonstrate the effectiveness of our ZTA framework:  
* Poisoning Resilience: Our framework exhibits significant resilience to poisoning attacks. With up to 15% malicious participants, the system maintains **94% global accuracy**, outperforming the baseline system [11] by **27%** (Figure 14). This enhanced resilience is crucial for FL scenarios, where data poisoning remains one of the major security threats. The robustness of our system ensures that malicious actors cannot easily degrade model performance, thus maintaining trust in the federated environment. * Access Control Enhancement: One of the primary contributions of our framework is the integration of continuous behavioral authentication. This significantly enhances access control, reducing unauthorized access attempts by **89%**, as depicted in Figure 15. The ability to dynamically authenticate user behavior, rather than relying solely on static credentials, adds a layer of security that is particularly effective against insider threats. Moreover, the system initiates containment actions within **200 ms** of detecting an unauthorized access attempt, significantly reducing the potential damage from any attack. * Advanced Anomaly Detection: Our anomaly detection mechanism, based on an autoencoder model, achieves a **94% attack detection F1-score**, surpassing existing anomaly detectors, such as the xApp anomaly detectors [60], by **16%**. This high detection rate indicates the system's ability to accurately distinguish between normal and abnormal behavior in real-time, even in the presence of sophisticated attacks. This is crucial for detecting zero-day attacks, where no prior knowledge of the attack is available. * Zero-Day Attack Simulations: In zero-day attack scenarios, where attackers exploit previously unknown vulnerabilities, our system demonstrates a **76% success rate** in preemptively isolating compromised nodes before the attack can propagate. This proactive isolation significantly reduces the potential impact of such attacks, limiting the damage to the network and maintaining system integrity. Our results are compared against some of the most widely cited security frameworks in the literature, including RBAC [15] and traditional no-defense systems. As shown in Figure 13, our ZTA framework reduces the attack success rate to 11%, which is a significant improvement over the 87% success rate seen in RBAC and the 100% success rate in systems without defense mechanisms. This demonstrates that our framework provides a far more effective solution for preventing attacks, especially in high-risk environments. In terms of poisoning resilience (Figure 14), our ZTA framework significantly outperforms the baseline approach by maintaining a high global accuracy despite the presence of malicious participants. The baseline system's accuracy drops sharply as the number of malicious participants increases, whereas our framework retains a relatively stable performance, proving its robustness in FL scenarios where adversarial participants may try to degrade the model. When it comes to access control, our ZTA framework also outperforms RBAC and no-defense systems by a large margin. As depicted in Figure 15, unauthorized access attempts are reduced by 89% with continuous behavioral authentication. This offers a level of security that is beyond traditional RBAC, which relies on static roles and permissions and is vulnerable to insider attacks or stolen credentials. While our security framework has shown promising results, there are several areas for future enhancement.