53 citations · 93 across the 7 of their papers we have counts for
6 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…
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…
Understanding Real-world Threats to Deep Learning Models in Android Apps
Zizhuang Deng, Kai Chen, Guozhu Meng +3
Famous for its superior performance, deep learning (DL) has been popularly used within many applications, which also at the same time attracts various threats to the models. One pr…
Mitigating Adversarial Attacks by Distributing Different Copies to Different Users
Jiyi Zhang, Han Fang, Wesley Joon-Wie Tann +3
Machine learning models are vulnerable to adversarial attacks. In this paper, we consider the scenario where a model is distributed to multiple buyers, among which a malicious buye…