3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
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…
LogShield: A Transformer-based APT Detection System Leveraging Self-Attention
Sihat Afnan, Mushtari Sadia, Shahrear Iqbal +1
Cyber attacks are often identified using system and network logs. There have been significant prior works that utilize provenance graphs and ML techniques to detect attacks, specif…