P3GNN: A Privacy-Preserving Provenance Graph-Based Model for APT Detection in Software Defined Networking

arXiv:2406.12003v1 Announce Type: new
Abstract: Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advanced Persistent Threats (APTs), sophisticated and stealthy cyberattacks that traditional detection methods often fail to counter, especially in the face of zero-day exploits. A prevalent issue is the inadequacy of existing strategies to detect novel threats while addressing data privacy concerns in collaborative learning scenarios. This paper presents P3GNN (privacy-preserving provenance graph-based graph neural …

advanced advanced persistent threats advancements apt apts arxiv counter cs.cr cyberattacks defined detection evolution fail graph management network networking network management persistent persistent threats privacy provenance sdn software threats vulnerability