Can Adversarial Network Attack be Defended?

TL;DR

This paper explores defense strategies for graph neural networks against adversarial attacks, proposing novel adversarial training and smoothing techniques that improve robustness across various network analysis tasks.

Contribution

It introduces the first defense methods against network adversarial attacks, including novel adversarial training and smoothing strategies to enhance GNN robustness.

Findings

Proposed strategies significantly improve GNN robustness against attacks.

Smoothing distillation and cross-entropy reduce adversarial gradient amplitudes.

Experimental results on real-world networks validate effectiveness.

Abstract

Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since carefully crafted adversarial networks with slight perturbations on clean network may invalid lots of network applications, such as node classification, link prediction, and community detection etc. Such attacks are easily constructed with serious security threat to various analyze methods, including traditional methods and deep models. To the best of our knowledge, it is the first time that defense method against network adversarial attack is discussed. In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks. First, we propose novel adversarial training strategies to improve GNNs' defensibility against attacks. Then, we analytically investigate the robustness properties for GNNs granted by the use of smooth defense, and propose two special smooth defense strategies: smoothing distillation and smoothing cross-entropy loss function. Both of them are capable of smoothing gradient of GNNs, and consequently reduce the amplitude of adversarial gradients, which benefits gradient masking from attackers. The comprehensive experiments show that our proposed strategies have great defensibility against different adversarial attacks on four real-world networks in different network analyze tasks.