53 citations · 61 across the 8 of their papers we have counts for
11 papers
Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic
Yuqi Qing, Qilei Yin, Xinhao Deng +6
Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a su…
Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains
Peiyang Li, Ye Wang, Qi Li +5
Recently unsupervised machine learning based systems have been developed to detect zero-day Web attacks, which can effectively enhance existing Web Application Firewalls (WAFs). Ho…
Secure Inter-domain Routing and Forwarding via Verifiable Forwarding Commitments
Xiaoliang Wang, Zhuotao Liu, Qi Li +6
The Internet inter-domain routing system is vulnerable. On the control plane, the de facto Border Gateway Protocol (BGP) does not have built-in mechanisms to authenticate routing a…
martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture
Qi Li, Zhuotao Liu, Ke Xu
The development of machine learning models requires a large amount of training data. Data marketplaces are essential for trading high-quality, private-domain data not publicly avai…
CONTRACTFIX: A Framework for Automatically Fixing Vulnerabilities in Smart Contracts
Pengcheng, Peng, Yun +7
The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, deploying smart…
HyperService: Interoperability and Programmability Across Heterogeneous Blockchains
Zhuotao Liu, Yangxi Xiang, Jian Shi +5
Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing intero…