Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples raises …

A recent line of work focused on making adversarial training computationally efficient for
deep learning models. In particular, Wong et al.(2020) showed that $\ell_\infty $-adversarial …

We show how to turn any classifier that classifies well under Gaussian noise into a new
classifier that is certifiably robust to adversarial perturbations under the L2 norm. While this" …

Deep neural networks are revolutionizing the way complex systems are designed.
Consequently, there is a pressing need for tools and techniques for network analysis and …

Finding minimum distortion of adversarial examples and thus certifying robustness in neural
networks classifiers is known to be a challenging problem. Nevertheless, recently it has …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

Due to the increasing deployment of Deep Neural Networks (DNNs) in real-world security-
critical domains including autonomous vehicles and collision avoidance systems, formally …

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …

Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …