## 6 Case Studies in AI for Communication Networks
### Case Study 3: AI for Network Security in Cloud-Based Communications  
As cloud-based communication systems become more prevalent, securing data and ensuring privacy is a critical challenge. AI has been implemented to enhance network security in cloud environments, particularly in the areas of intrusion detection, anomaly detection, and data protection [135].  
AI-driven security systems can analyze incoming traffic for abnormal patterns, identify potential threats such as DDoS attacks, and dynamically adjust security measures to block malicious traffic. Additionally, AI plays a key role in ensuring the privacy of communication by implementing privacy-preserving techniques, including encryption and anonymization of sensitive data. AI-driven systems can also detect anomalies in cloud-based communications and provide real-time responses to mitigate potential risks [135].  
In addition to traditional AI techniques, federated learning has recently emerged as a promising approach to enhance security in cloud-based communications. Federated learning allows multiple devices or systems to collaboratively train machine learning models while keeping their data locally. This approach helps mitigate privacy concerns, as sensitive data do not leave the device but instead contribute to model updates in a decentralized manner. In cloud environments, federated learning can improve intrusion detection and anomaly detection systems by aggregating insights from distributed devices without exposing raw data. This ensures that AI models can learn from diverse data sources, making them more robust and accurate while preserving user privacy.  
Figure 12 represents an AI-driven network security framework designed to safeguard cloud-based communication systems. At the top, a labeled \"Cloud-Based Communication System\" encapsulates the cloud environment, symbolized by two servers (\"Server 1\" and \"Server 2\") that handle incoming traffic. The AI-powered Intrusion Detection System (IDS) is positioned below the servers, highlighting its role in scanning all incoming traffic for potential threats. Traffic from the servers flows directly to the IDS, where initial analysis takes place. Below the IDS is a \"Data Protection Layer\", which adds a security layer by securing data exchanges and monitoring for irregularities. Finally, an \"Anomaly Detection\" layer further examines the data to detect unusual patterns that could indicate security  
Figure 11: AI-driven security and management in IoT and edge networks.  
risks, ensuring comprehensive threat detection. Together, these interconnected components illustrate a multi-layered AI security strategy designed to enhance data integrity, prevent unauthorized access, and identify anomalies in real-time within a cloud-based communication infrastructure. This setup illustrates how each component contributes to creating a secure and reliable cloud communication system, with AI algorithms driving security operations at every level.  
Real-Time Security and Anomaly Detection in Cloud Communications  
In order to demonstrate the practical applicability of the proposed solutions, I conducted several experiments to validate the performance of the AI models in communication networks. These experiments focus on key metrics such as accuracy, latency, energy consumption, and scalability. The following subsections detail the experimental setup, results, and comparisons with existing approaches.