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Adversarial Robustness of Graph-based Anomaly Detection

by   Yulin Zhu, et al.

Graph-based anomaly detection is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques. These GAD tools, however, expose a new attacking surface, ironically due to their unique advantage of being able to exploit the relations among data. That is, attackers now can manipulate those relations (i.e., the structure of the graph) to allow target nodes to evade detection or degenerate the classification performance of the detection. In this paper, we exploit this vulnerability by designing the structural poisoning attacks to a FeXtra-based GAD system termed OddBall as well as the black box attacks against GCN-based GAD systems by attacking the imbalanced lienarized GCN ( LGCN ). Specifically, we formulate the attack against OddBall and LGCN as a one-level optimization problem by incorporating different regression techniques, where the key technical challenge is to efficiently solve the problem in a discrete domain. We propose a novel attack method termed BinarizedAttack based on gradient descent. Comparing to prior arts, BinarizedAttack can better use the gradient information, making it particularly suitable for solving discrete optimization problems, thus opening the door to studying a new type of attack against security analytic tools that rely on graph data.


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