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 …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at
training time. A backdoor attack installs a backdoor into the victim model by injecting a …

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time
attack that injects a trigger pattern into a small proportion of training data so as to control the …

The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
imperceptible perturbations. A range of defense techniques have been proposed to improve …

Adversarial attacks are valuable for evaluating the robustness of deep learning models.
Existing attacks are primarily conducted on the visible light spectrum (eg, pixel-wise texture …

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 …