8 citations · 9 across the 10 of their papers we have counts for
11 papers
ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning
Mohsen Salehi, Karthik Pattabiraman
Reinforcement learning (RL) controllers have been recently adopted for Unmanned Aerial Vehicles (UAV) navigation and control. However, they are susceptible to action-space attacks…
Function Name Is All You Need to Detect Blockchain Application Attacks
Rui Xi, Zehua Wang, Karthik Pattabiraman
Blockchain application attacks, targeting business logic bugs in decentralized applications (dApps), have been an increasing concern to their developers and users, causing signific…
Chameleon: Recovering Cyber-Physical Systems from Memory Corruption Attacks via ML Surrogates
Mohsen Salehi, Karthik Pattabiraman
Cyber-physical systems (CPSs) can be compromised through memory corruption vulnerabilities, which can result in safety violations. Existing techniques mostly focus on detecting suc…
Framework for Discovering GPS Spoofing Attacks in Drone Swarms
Yingao Elaine Yao, Pritam Dash, Karthik Pattabiraman
Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved int…
SAMD: A Tool for Identifying False Data Injection Scenarios in AI/ML-enabled Medical Devices
Mohammadreza Hallajiyan, Xueren Ge, Athish Pranav Dharmalingam +4
The growing integration of artificial intelligence (AI) and machine learning (ML) in medical systems requires effective measures to address emerging security risks. One such risk i…
ROAST: Risk-aware Outlier-exposure for Adversarial Selective Training of Anomaly Detectors Against Evasion Attacks
Mohammed Elnawawy, Gargi Mitra, Shahrear Iqbal +1
Safety-critical domains like healthcare rely on deep neural networks (DNNs) for prediction, yet DNNs remain vulnerable to evasion attacks. Anomaly detectors (ADs) are widely used t…
Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices
Gargi Mitra, Mohammadreza Hallajiyan, Inji Kim +5
The integration of AI/ML into medical devices is rapidly transforming healthcare by enhancing diagnostic and treatment facilities. However, this advancement also introduces serious…
ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks
Pritam Dash, Ethan Chan, Nathan P. Lawrence +1
Unmanned Aerial Vehicles (UAVs) depend on onboard sensors for perception, navigation, and control. However, these sensors are susceptible to physical attacks, such as GPS spoofing,…
Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs
Mohammed Elnawawy, Gargi Mitra, Shahrear Iqbal +1
Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks (DNN) to make predictions and infer decisions. DNNs are susceptible to evasion atta…
Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models
Qutub Syed Sha, Michael Paulitsch, Karthik Pattabiraman +6
As transformer-based object detection models progress, their impact in critical sectors like autonomous vehicles and aviation is expected to grow. Soft errors causing bit flips dur…
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks
Florian Geissler, Syed Qutub, Michael Paulitsch +1
We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet…