activity
20182021
most citedSG-PBFT: a Secure and Highly Efficient Blockchain PBFT Consensus Algorithm for Internet of Vehicles

9 citations · 12 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CR20213 cited

OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS

Guangquan Xu, GuoHua Xin, Litao Jiao +4

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (D…

cs.CR20219 cited

SG-PBFT: a Secure and Highly Efficient Blockchain PBFT Consensus Algorithm for Internet of Vehicles

Guangquan Xu, Yihua Liu, Jun Xing +5

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough…

cs.DC2021

DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling

Shangming Cai, Dongsheng Wang, Haixia Wang +4

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared mod…

cs.CR2020

SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space

Ao Liu, Yunpeng Wang, Tao Li

In the encrypted network traffic intrusion detection, deep learning based schemes have attracted lots of attention. However, in real-world scenarios, data is often insufficient (fe…

cs.CY2019

Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies

Hong-Ning Dai, Hao Wang, Guangquan Xu +2

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufac…

cs.CR2018

Roundtable Gossip Algorithm: A Novel Sparse Trust Mining Method for Large-scale Recommendation Systems

Mengdi Liu, Guangquan Xu

Cold Start (CS) and sparse evaluation problems dramatically degrade recommendation performance in large-scale recommendation systems such as Taobao and eBay. We name this degradati…