A Privacy-Preserving Graph Neural Network for Network Intrusion Detection

With the ever-growing attention on communication security, machine learning-based network intrusion detection system (NIDS) is widely utilized to meet different security requirements. However, most of the existing methods manually extract or learn features from raw traffic, which is usually expensive, complicated, and time-consuming. Moreover, this also brings unprecedented challenges for preserving users’ privacy in the communication process, making it difficult for existing solutions to be deployed in practice due to the privacy requirements from legal policies. This paper proposes a privacy-preserving graph neural network (named NIGNN) for NIDS, which can encode the local structure and traffic features. To address the privacy issues pertaining to the application of graph representation learning, we design a privacy message-passing mechanism with formal privacy guarantees, in which sensitive information potentially contained in graph vertices will be kept private. Specifically, we design a privacy-enhancement graph representation that introduces a degree-sensitive item in vertex-based aggregation to reduce noise. Our theoretical analysis shows that NIGNN can provide a provable privacy guarantee. Extensive experiments demonstrate NIGNN's performance in maintaining a sound privacy-accuracy trade-off.