activity
20182021
most citedPFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems

11 citations · 22 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV2021

Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications

Gautham Ramajayam, Tao Sun, Chiu C. Tan +2

Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information th…

cs.CR20213 cited

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

Haotian Chi, Qiang Zeng, Xiaojiang Du +1

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT pl…

cs.CR201911 cited

PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems

Haotian Chi, Qiang Zeng, Xiaojiang Du +1

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious priv…

cs.CR20186 cited

A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis

Kimberly Redmond, Lannan Luo, Qiang Zeng

Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware…

cs.CR20182 cited

Code-less Patching for Heap Vulnerabilities Using Targeted Calling Context Encoding

Qiang Zeng, Golam Kayas, Emil Mohammed +3

Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure, requiring intensive manua…

cs.SD2018

A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples

Qiang Zeng, Jianhai Su, Chenglong Fu +2

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become…