In this study, we conduct a comprehensive review of smart grid security, exploring system
architectures, attack methodologies, defense strategies, and future research opportunities …

Abstract Machine learning and deep learning models are increasingly susceptible to
adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder …

With the rise of Machine Learning as a Service (MLaaS) platforms, safeguarding the
intellectual property of deep learning models is becoming paramount. Among various …

Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is
an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they …

Despite significant advancements in out-of-distribution (OOD) detection, existing methods
still struggle to maintain robustness against adversarial attacks, compromising their …

We consider the verification of neural network policies for reach-avoid control tasks in
stochastic dynamical systems. We use a verification procedure that trains another neural …

Ensemble learning has achieved remarkable success in machine learning, but its reliance
on numerous base learners limits its application in resource-constrained environments. This …