The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

The increasing utilization of deep neural networks (DNNs) in safety-critical systems has
raised concerns about their potential to exhibit undesirable behaviors. Consequently, DNN …

Robustness is widely regarded as a fundamental problem in the analysis of machine
learning (ML) models. Most often robustness equates with deciding the non-existence of …

Learning-enabled controllers have been adopted in various cyber-physical systems (CPS).
When a learning-enabled controller fails to accomplish its task from a set of initial states …

Many works have been devoted to evaluating the robustness of a classifier in the
neighborhood of single points of input data. Recently, in particular, probabilistic settings …

Neural networks are being applied to a wide range of tasks in autonomous systems, such as
perception, prediction, planning, control, and general decision making. While they may …

Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating
cyber threats in dynamic network environments. However, maintaining high performance …

Models of actual causality leverage domain knowledge to generate convincing diagnoses of
events that caused an outcome. It is promising to apply these models to diagnose and repair …

Deep neural networks have demonstrated impressive performance in a wide variety of
applications. However, deep neural networks are not perfect. In many cases, additional …

Autonomous systems powered by Artificial Neural Networks (NNs) have shown remarkable
capabilities in performing complex tasks that are difficult to formally specify. However …