Understanding Universal Adversarial Attack and Defense on Graph

Abstract

Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph universal adversarial attack is a special type of attack on graph which can attack any targeted victim by flipping edges connected to anchor nodes. In this paper, we propose the forward-derivative-based graph universal adversarial attack (FDGUA). Firstly, we point out that one node as training data is sufficient to generate an effective continuous attack vector. Then we discretize the continuous attack vector based on forward derivative. FDGUA can achieve impressive attack performance that three anchor nodes can result in attack success rate higher than 80% for the dataset Cora. Moreover, we propose the first graph universal adversarial training (GUAT) to defend against universal adversarial attack. Experiments show that GUAT can effectively improve the robustness of the GNNs without degrading the accuracy of the model.

A Network Intrusion Detection Method for Information Systems Using Federated Learning and Improved Transformer

“…Traditional intrusion detection methods are based on fixed or dynamic rules to identify attacks on the network (Sawsan, et al, 2020). However, attackers use various techniques to disguise their attacks and disrupt the target's defense system (Xu, et al, 2021;Singh, & Gupta, 2022;Wang, et al, 2022). Therefore, ML algorithms were first widely used to detect anomalies in networks and have been proven to provide high detection rates.…”