53 citations · 93 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis
Chuanpu Fu, Qi Li, Ke Xu
In this paper, we propose HyperVision, a realtime unsupervised machine learning (ML) based malicious traffic detection system. Particularly, HyperVision is able to detect unknown p…