activity
20182021
most citedSmartphone Impostor Detection with Built-in Sensors and Deep Learning

3 citations · 7 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CR2021

CloudShield: Real-time Anomaly Detection in the Cloud

Zecheng He, Ruby B. Lee

In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, i…

cs.CR20211 cited

Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

Guangyuan Hu, Zecheng He, Ruby B. Lee

Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism t…

cs.CL20211 cited

ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces

Zecheng He, Srinivas Sunkara, Xiaoxue Zang +7

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibil…

cs.CV2020

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

Qingsong Yao, Zecheng He, Yi Lin +3

Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical A…

cs.CR2020

New Models for Understanding and Reasoning about Speculative Execution Attacks

Zecheng He, Guangyuan Hu, Ruby Lee

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through cov…

cs.CV20202 cited

Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

Qingsong Yao, Zecheng He, Hu Han +1

Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnera…