A Peer to Peer Federated Graph Neural Network for Threat Intelligence

A Peer to Peer Federated Graph Neural Network for Threat Intelligence

연구 분야: Safety

학회: International Conference on Networked Systems

Threat intelligence is the process of collecting and analyzing information about potential cyber threats. Several approaches have been conducted for cyber threat detection based on the federated learning method. These approaches aim to establish a collaborative threat intelligence sharing between the participants, in order to reinforce their security defense systems. However, these approaches face scalability limitations and raise security and privacy issues: availability, inference attacks, poisoning attacks. To address these issues, we propose a peer-to-peer federated graph neural network (FGNN) approach for threat intelligence. The approach incorporates techniques to ensure data security and privacy. It includes secure aggregation methods and a decentralized sampling technique to reduce the number of exchanged messages. This approach includes also a reputation scoring technique to detect and prevent poisoning attacks, which makes it resilient in the presence of malicious participants.