491 citations · 714 across the 19 of their papers we have counts for
19 papers
SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks
Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla +2
Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to vo…
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance
Ashish Hooda, Andrey Labunets, Tadayoshi Kohno +1
Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers. Perceptual hashi…
Experimental Security Analysis of the App Model in Business Collaboration Platforms
Yunang Chen, Yue Gao, Nick Ceccio +3
Business Collaboration Platforms like Microsoft Teams and Slack enable teamwork by supporting text chatting and third-party resource integration. A user can access online file stor…
Exploring Adversarial Robustness of Deep Metric Learning
Thomas Kobber Panum, Zi Wang, Pengyu Kan +2
Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples. DML uses deep neural architectures to learn semantic embeddings o…
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
Yuzhe Ma, Jon Sharp, Ruizhe Wang +2
Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component…
Data Privacy in Trigger-Action Systems
Yunang Chen, Amrita Roy Chowdhury, Ruizhe Wang +3
Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation. They provide a simple interface that helps end-users crea…
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
Athena Sayles, Ashish Hooda, Mohit Gupta +2
Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses o…
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
Ryan Feng, Neal Mangaokar, Jiefeng Chen +3
This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the r…
New Problems and Solutions in IoT Security and Privacy
Earlence Fernandes, Amir Rahmati, Nick Feamster
In a previous article for S&P magazine, we made a case for the new intellectual challenges in the Internet of Things security research. In this article, we revisit our earlier obse…
Analyzing the Interpretability Robustness of Self-Explaining Models
Haizhong Zheng, Earlence Fernandes, Atul Prakash
Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness. We evaluate the interpretability robu…
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
Z. Berkay Celik, Earlence Fernandes, Eric Pauley +2
Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive---new app…
Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attack…
Tyche: Risk-Based Permissions for Smart Home Platforms
Amir Rahmati, Earlence Fernandes, Kevin Eykholt +1
Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smart…
Note on Attacking Object Detectors with Adversarial Stickers
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable…
IFTTT vs. Zapier: A Comparative Study of Trigger-Action Programming Frameworks
Amir Rahmati, Earlence Fernandes, Jaeyeon Jung +1
The growing popularity of online services and IoT platforms along with increased developer's access to devices and services through RESTful APIs is giving rise to a new class of fr…
Decoupled-IFTTT: Constraining Privilege in Trigger-Action Platforms for the Internet of Things
Earlence Fernandes, Amir Rahmati, Jaeyeon Jung +1
Trigger-Action platforms are an emerging class of web-based systems that enable users to create automation rules (or recipes) of the form, "If there is a smoke alarm, then turn off…
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. G…
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
Earlence Fernandes, Amir Rahmati, Kevin Eykholt +1
The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into…
Anception: Application Virtualization For Android
Earlence Fernandes, Alexander Crowell, Ajit Aluri +1
The problem of malware has become significant on Android devices. Library operating systems and application virtualization are both possible solutions for confining malware. Unfort…