Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs

arXiv:2402.18986v1 Announce Type: new
Abstract: Graph neural network-based network intrusion detection systems have recently demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these methods suffer from a reliance on target encoding for data pre-processing, limiting widespread adoption due to the associated need for annotated labels--a cost-prohibitive requirement. In this work, we propose a solution involving in-context pre-training and the utilization of dense representations for categorical features to jointly overcome the label-dependency limitation. Our approach exhibits remarkable data efficiency, achieving over 98% …

adoption art arxiv benchmark cost cs.cr data datasets detection encoding graph intrusion intrusion detection network neural network performance representation state systems target training work