7 citations · 37 across the 15 of their papers we have counts for
16 papers
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs
Hongyan Bao, Yufei Han, Yujun Zhou +2
Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically…
Cerberus: Exploring Federated Prediction of Security Events
Mohammad Naseri, Yufei Han, Enrico Mariconti +3
Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction mod…
Finding MNEMON: Reviving Memories of Node Embeddings
Yun Shen, Yufei Han, Zhikun Zhang +5
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of g…
Model Stealing Attacks Against Inductive Graph Neural Networks
Yun Shen, Xinlei He, Yufei Han +1
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to buil…
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
Zhuo Yang, Yufei Han, Xiangliang Zhang
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label l…
Characterizing the Evasion Attackability of Multi-label Classifiers
Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackabi…