Attacking Graph-based Classification via Manipulating the Graph Structure

Graph-based classification methods are widely used for security analytics. Roughly speaking, graph-based classification methods include collective classification and graph neural network. Attacking a graph-based classification method enables an attacker to evade detection in security analytics. However, 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, leaving collective classification largely unexplored. We aim to bridge this gap in this work. We consider 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. However, it is computationally challenging to solve the optimization problem exactly. To address the challenge, 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 using four graph datasets. Our results show that our attacks can effectively evade graph-based classification methods. Moreover, our attacks outperform the existing attack for evading collective classification methods and some graph neural network methods. CCS CONCEPTS • Security and privacy; • Computing methodologies → Machine learning;