Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

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

Deep neural networks are vulnerable to adversarial examples that mislead the models with
imperceptible perturbations. Though adversarial attacks have achieved incredible success …

For black-box attacks, the gap between the substitute model and the victim model is usually
large, which manifests as a weak attack performance. Motivated by the observation that the …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Adversarial training, in which a network is trained on adversarial examples, is one of the few
defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high …

Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers
by adding imperceptible perturbations. An intriguing property of adversarial examples is …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

The goal of a decision-based adversarial attack on a trained model is to generate
adversarial examples based solely on observing output labels returned by the targeted …