Adversarial attacks and defenses on graphs
Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei **†, Yaxin Li†, Han Xu†, Yiqi Wang† …
Graphs: A Review, A Tool and Empirical Studies Wei **†, Yaxin Li†, Han Xu†, Yiqi Wang† …
A survey of adversarial learning on graphs
Local model poisoning attacks to {Byzantine-Robust} federated learning
In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …
client device maintains a local model for its local training dataset, while a master device …
Kairos: Practical intrusion detection and investigation using whole-system provenance
Provenance graphs are structured audit logs that describe the history of a system's
execution. Recent studies have explored a variety of techniques to analyze provenance …
execution. Recent studies have explored a variety of techniques to analyze provenance …
“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Adversarial attack and defense on graph data: A survey
Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …
image classification, text generation, audio recognition, and graph data analysis. However …
Baffle: Backdoor detection via feedback-based federated learning
Recent studies have shown that federated learning (FL) is vulnerable to poisoning attacks
that inject a backdoor into the global model. These attacks are effective even when …
that inject a backdoor into the global model. These attacks are effective even when …
Backdoor attacks to graph neural networks
In this work, we propose the first backdoor attack to graph neural networks (GNN).
Specifically, we propose a subgraph based backdoor attack to GNN for graph classification …
Specifically, we propose a subgraph based backdoor attack to GNN for graph classification …