206 citations · 246 across the 18 of their papers we have counts for
8 papers · 1 filter
RVDebloater: Mode-based Adaptive Firmware Debloating for Robotic Vehicles
Mohsen Salehi, Karthik Pattabiraman
As the number of embedded devices grows and their functional requirements increase, embedded firmware is becoming increasingly larger, thereby expanding its attack surface. Despite…
Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models
Zitao Chen, Karthik Pattabiraman
The rise of deep learning (DL) has led to a surging demand for training data, which incentivizes the creators of DL models to trawl through the Internet for training materials. Mea…
AutoPatch: Automated Generation of Hotpatches for Real-Time Embedded Devices
Mohsen Salehi, Karthik Pattabiraman
Real-time embedded devices like medical or industrial devices are increasingly targeted by cyber-attacks. Prompt patching is crucial to mitigate the serious consequences of such at…
A Method to Facilitate Membership Inference Attacks in Deep Learning Models
Zitao Chen, Karthik Pattabiraman
Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and code repositories that can greatly facilitate the development of ML models. Today, even ordinary…
Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems
Mohammed Elnawawy, Mohammadreza Hallajiyan, Gargi Mitra +2
The adoption of machine-learning-enabled systems in the healthcare domain is on the rise. While the use of ML in healthcare has several benefits, it also expands the threat surface…
ICS-Sniper: A Targeted Blackhole Attack on Encrypted ICS Traffic
Gargi Mitra, Chanyuan Liu, Pritam Dash +4
Modern industrial control systems (ICS) increasingly host their Supervisory Control and Data Acquisition (SCADA) services in the cloud to reduce the costs of large-scale automation…
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction
Zitao Chen, Karthik Pattabiraman
Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model. While there have be…
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks
Zitao Chen, Pritam Dash, Karthik Pattabiraman
Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are…