公开(公告)号：US20250110991A1

公开(公告)日：2025-04-03

申请号：US18884599

申请日：2024-09-13

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Husrev Taha Sencar ,  Enes Altinisik ,  Fatih Deniz

IPC: G06F16/903 ,  G06F16/901

Abstract: Example systems, methods, and apparatus are disclosed herein for ProvG-Searcher for querying large provenance graph repositories. A system for querying large provenance graph repositories including a server, a processor, and a memory storing instruction that, when executed by the processor, cause the processor to apply an embedding function and apply a subgraph prediction function.

公开(公告)号：US20240333749A1

公开(公告)日：2024-10-03

申请号：US18617133

申请日：2024-03-26

Applicant: Qatar Foundation for Education, Science and Community Development

Inventor： Mohamed Nabeel ,  Issa Khalil ,  Ting Yu ,  Fatih Deniz

IPC: H04L9/40 ,  H04L41/16

CPC classification number: H04L63/1433 ,  H04L41/16 ,  H04L63/145

Abstract: Proactively detecting malicious domains using graph representation learning may be provided by extracting seed domains from a uniform resource locator (URL) feed of observed requests for access to domains; expanding the seed domains to a via a passive domain name service (PDNS) crawl to include additional domains with the seed domains; collecting a ground truth, including labeling a first set of the seed domains as benign and a second set of the seed domains as malicious; constructing a graph neural network (GNN) of the additional domains and the seed domains, wherein each domain of the additional domains and the seed domains are represented as a node in the GNN that includes feature values associated that domain; training the GNN to classify unseen domains not associated with a node as either benign or malicious; and classifying, via the GNN, a queried domain as either benign or malicious.