Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringGraph ClusteringSimilar Papers 제목 키워드 기반
Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms
Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clus…
ClusteringData PoisoningTowards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography
Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems due to their advanced visual-language reasoning capabilities, targeting the perception, prediction, planni…
Autonomous DrivingDecision MakingNode-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Deep Graph Learning (DGL) has emerged as a crucial technique across various domains. However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility to evasion and poisoning attacks. While empi…
Graph LearningNode ClassificationRecommendation SystemsHow does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications
We bridge two research directions on graph neural networks (GNNs), by formalizing the relation between heterophily of node labels (i.e., connected nodes tend to have dissimilar labels) and the robustness of GNNs to adver…
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
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 …
Anomaly DetectionGraph Anomaly Detection