Enhancing Intrusion Detection in Cloud Infrastructure through Federated Learning and Behavioral Analytics

ABSTRACT: The high rate at which cloud computing is gaining popularity has ushered immense security concerns, especially in identifying elaborate security threats without jeopardizing data security. Data visualization detection systems (IDS) have problems related to scalability, false positives, and compliance in the distributed cloud. The present article investigates using Federated Learning (FL) and Behavioral Analytics (BA) to improve cloud IDS. FL allows decentralized (in-the-cloud) model training to be privacy-preserving, whereas BA makes it possible to discover dynamic threats based on behavioral anomalies analysis. The FL-BA framework is devoted to the most urgent gaps in modern systems, such as data silos, privacy violations, as well as adaptive learning limitations. The most significant benefits are the increment in the accuracy of detections by up to 28 percent on advanced threats, GDPR/HIPAA compliance, and a decrease in communication overhead. The difficulties like non-IID data distribution and adversarial attacks are mentioned, as well as such future directions as hybrid architectures and blockchain-based governance. This paper showed the impact of FL-BA systems in transforming cloud security that manages the tradeoffs between performance and privacy in favor of adaptability.