There is a rising interest in studying the robustness of deep neural network classifiers
against adversaries, with both advanced attack and defence techniques being actively …

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which
provides a way to evaluate the adversarial robustness. In practice, attack algorithms are …

Generative Adversarial Networks (GANs) have seen significant interest since their
introduction in 2014. While originally focused primarily on image-based tasks, their capacity …

Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

Deep learning technology has become an important branch of artificial intelligence.
However, researchers found that deep neural networks, as the core algorithm of deep …

Abstract Network Intrusion Detection System (NIDS) is a key component in securing the
computer network from various cyber security threats and network attacks. However …

Following the recent adoption of deep neural networks (DNN) accross a wide range of
applications, adversarial attacks against these models have proven to be an indisputable …

Deep learning has evolved as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

The problem of adversarial examples, evasion attacks on machine learning classifiers, has
proven extremely difficult to solve. This is true even in the black-box threat model, as is the …

Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …