Design of an Efficient Model for Federated Deep Graph Learning in Application-Level Security

Abstract

The need for this work stems from the ever-increasing complexity of cyber threats and the critical importance of safeguarding sensitive data while improving threat detection accuracy in the realm of application-level security. Existing methods often fall short in addressing these challenges, primarily due to limitations in data privacy, threat detection accuracy, and adaptability to emerging threats. In response, this paper presents a pioneering approach – the Federated Deep Graph Learning System. This novel system seamlessly integrates the capabilities of Deep Graph Neural Networks (GNNs) with federated learning techniques, offering a robust solution to the limitations of previous approaches. GNNs excel in uncovering intricate network patterns, enhancing threat detection accuracy, while federated learning ensures data privacy and security compliance. The proposed system not only improves threat detection accuracy by 4.9% precision, 5.5% accuracy, and 5.9% recall but also exhibits a 3.5% increase in speed and an 8.5% better AUC when compared to existing methods. Furthermore, it offers dynamic adaptability to evolving threats and promotes cross-organizational collaboration without compromising sensitive data, promising a comprehensive and efficient cyber defense network. This work represents a significant advancement in the field of application-level security, addressing the limitations of existing approaches and shaping the future of cybersecurity.