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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…
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…