Attacking Graph-based Classification via Manipulating the Graph Structure
This work addresses security and privacy vulnerabilities in graph analytics, enabling attackers to evade detection or defend against inference attacks, though it is incremental as it extends adversarial machine learning to an underexplored area.
The authors tackled the problem of evading graph-based classification methods, particularly collective classification, by manipulating graph structure, achieving effective evasion without needing full access to model parameters or training data, and outperforming existing attacks in some cases.
Graph-based classification methods are widely used for security and privacy analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Evading a graph-based classification method enables an attacker to evade detection in security analytics and can be used as a privacy defense against inference attacks. Existing adversarial machine learning studies mainly focused on machine learning for non-graph data. Only a few recent studies touched adversarial graph-based classification methods. However, they focused on graph neural network methods, leaving adversarial collective classification largely unexplored. We aim to bridge this gap in this work. We first propose a threat model to characterize the attack surface of a collective classification method. Specifically, we characterize an attacker's background knowledge along three dimensions: parameters of the method, training dataset, and the complete graph; an attacker's goal is to evade detection via manipulating the graph structure. We formulate our attack as a graph-based optimization problem, solving which produces the edges that an attacker needs to manipulate to achieve its attack goal. Moreover, we propose several approximation techniques to solve the optimization problem. We evaluate our attacks and compare them with a recent attack designed for graph neural networks. Results show that our attacks 1) can effectively evade graph-based classification methods; 2) do not require access to the true parameters, true training dataset, and/or complete graph; and 3) outperform the existing attack for evading collective classification methods and some graph neural network methods. We also apply our attacks to evade Sybil detection using a large-scale Twitter dataset and apply our attacks as a defense against attribute inference attacks using a large-scale Google+ dataset.