Coupled-Space Attacks against Random-Walk-based Anomaly Detection

TL;DR

This paper investigates the vulnerability of Random Walks-based Anomaly Detection to coupled-space attacks, proposing new attack strategies and demonstrating their effectiveness through comprehensive experiments.

Contribution

It introduces the first practical coupled-space attack framework against RWAD, including complexity analysis, bi-level optimization formulation, and attack strategies.

Findings

Proposed attacks effectively reduce anomaly scores of target nodes.

Graph-guided feature attacks outperform standalone attacks.

Attacks remain effective in black-box transfer scenarios.

Abstract

Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph can either be pre-existing or constructed from raw features. Consequently, there are two potential attack surfaces against RWAD: graph-space attacks and feature-space attacks. In this paper, we explore this vulnerability by designing practical coupled-space attacks, investigating the interplay between graph-space and feature-space attacks. To this end, we conduct a thorough complexity analysis, proving that attacking RWAD is NP-hard. Then, we proceed to formulate the graph-space attack as a bi-level optimization problem and propose two strategies to solve it: alternative iteration (alterI-attack) or utilizing the closed-form solution of the random walk model (cf-attack). Finally, we utilize the results from the graph-space attacks as guidance to design more powerful feature-space attacks (i.e., graph-guided attacks). Comprehensive experiments demonstrate that our proposed attacks are effective in enabling the target nodes from RWAD with a limited attack budget. In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes. Our study opens the door to studying the coupled-space attack against graph anomaly detection in which the graph space relies on the feature space.