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 …

Abstract He et al.(2018) have called into question the utility of pre-training by showing that
training from scratch can often yield similar performance to pre-training. We show that …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to
a false sense of security in defenses against adversarial examples. While defenses that …

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 …

Adversarial examples that fool machine learning models, particularly deep neural networks,
have been a topic of intense research interest, with attacks and defenses being developed …

In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Standard methods for generating adversarial examples for neural networks do not
consistently fool neural network classifiers in the physical world due to a combination of …

Neural networks are known to be vulnerable to adversarial examples: inputs that are close
to natural inputs but classified incorrectly. In order to better understand the space of …