111 citations · 154 across the 10 of their papers we have counts for
10 papers
Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks
Jiachen Li, Bang Wu, Xiaoyu Xia +3
Spiking Neural Networks (SNNs) have gained increasing attention for their superior energy efficiency compared to Artificial Neural Networks (ANNs). However, their security aspects,…
DeFiGuard: A Price Manipulation Detection Service in DeFi using Graph Neural Networks
Dabao Wang, Bang Wu, Xingliang Yuan +3
The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with reported losses surpassing 3.2 billion USD between 2018 and 2022 due to vulnerabilities in Decentraliz…
Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation
He Zhang, Bang Wu, Xiangwen Yang +3
Dynamic graph neural networks (DGNNs) have emerged and been widely deployed in various web applications (e.g., Reddit) to serve users (e.g., personalized content delivery) due to t…
Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification
Bang Wu, Xingliang Yuan, Shuo Wang +3
The deployment of Graph Neural Networks (GNNs) within Machine Learning as a Service (MLaaS) has opened up new attack surfaces and an escalation in security concerns regarding model…
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
Bang Wu, He Zhang, Xiangwen Yang +4
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse…
Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information
Heng Li, Zhang Cheng, Bang Wu +4
The function call graph (FCG) based Android malware detection methods have recently attracted increasing attention due to their promising performance. However, these methods are su…
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan +3
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation syst…
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
Bang Wu, Xiangwen Yang, Shirui Pan +1
Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and i…
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
Bang Wu, Xiangwen Yang, Shirui Pan +1
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker…
Defeating Misclassification Attacks Against Transfer Learning
Bang Wu, Shuo Wang, Xingliang Yuan +3
Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). Howev…