Graph Representation Learning on Novel Feature Based Graphs for Network Intrusion Detection

Ryan Hosler, Agnideven Palanisamy Sundar, Xukai Zou, Feng Li, Tianchong Gao

Abstract: Network Intrusions are an ever present threat in the modern age of instant transmission of data over the cyberspace. Ideally, an effective cybersecurity mechanism will detect an attack before it affects a given network. Hence, organizations utilize Network Intrusion Detection Systems (NIDS) to monitor incoming network traffic for all potential misuses. For this research, we present a novel method for aggregating network traffic into a graph for representation learning capable of outperforming existing NIDS in literature. We apply and validate our methods on numerous publically available network flow datasets for demonstrable and concrete performance evaluation.