Enhancing Digital Defense Using Federated Learning Security Sandbox

This academic poster presents a security framework that integrates Federated Learning into a sandbox environment for enhanced threat detection and mitigation. The system aims to protect user data privacy while enabling collaborative malware detection across multiple clients. Key components include file behavior analysis, entropy checks, network activity, and process injection detection. The proposed approach reduces the need for centralized data collection and provides a scalable, privacy-preserving cybersecurity model.