54 citations · 96 across the 11 of their papers we have counts for
11 papers
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +4
Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however,…
Supervised Robustness-preserving Data-free Neural Network Pruning
Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +1
When deploying pre-trained neural network models in real-world applications, model consumers often encounter resource-constraint platforms such as mobile and smart devices. They ty…
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
Yanjun Zhang, Guangdong Bai, Xue Li +2
Federated learning enables multiple participants to collaboratively train a model without aggregating the training data. Although the training data are kept within each participant…
ColdPress: An Extensible Malware Analysis Platform for Threat Intelligence
Haoxi Tan, Mahin Chandramohan, Cristina Cifuentes +2
Malware analysis is still largely a manual task. This slow and inefficient approach does not scale to the exponential rise in the rate of new unique malware generated. Hence, autom…
An Analytics Framework for Heuristic Inference Attacks against Industrial Control Systems
Taejun Choi, Guangdong Bai, Ryan K L Ko +3
Industrial control systems (ICS) of critical infrastructure are increasingly connected to the Internet for remote site management at scale. However, cyber attacks against ICS - esp…
When the Open Source Community Meets COVID-19: Characterizing COVID-19 themed GitHub Repositories
Liu Wang, Ruiqing Li, Jiaxin Zhu +2
Ever since the beginning of the outbreak of the COVID-19 pandemic, researchers from interdisciplinary domains have worked together to fight against the crisis. The open source comm…
PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
Yanjun Zhang, Guangdong Bai, Xue Li +3
Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should…
Don't Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams
Pengcheng Xia, Haoyu Wang, Xiapu Luo +6
As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency sca…
DEPOSafe: Demystifying the Fake Deposit Vulnerability in Ethereum Smart Contracts
Ru Ji, Ningyu He, Lei Wu +3
Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem. Besides Bitcoin, thousands of cryptocurre…
Analyzing Use of High Privileges on Android: An Empirical Case Study of Screenshot and Screen Recording Applications
Mark Huasong Meng, Guangdong Bai, Joseph K. Liu +2
The number of Android smartphone and tablet users has experienced a rapid growth in the past few years and it raises users' awareness on the privacy and security of their mobile de…
TwiInsight: Discovering Topics and Sentiments from Social Media Datasets
Zhengkui Wang, Guangdong Bai, Soumyadeb Chowdhury +2
Social media platforms contain a great wealth of information which provides opportunities for us to explore hidden patterns or unknown correlations, and understand people's satisfa…