… As depicted in Figure 1, an adversary (denoted as attacker) can mount MITM (Man-in-the-Middle)
attacks [12] to gather network traffic even in the case of a communication encrypted …

… This paper explored how adversarial attacks can be used to target supervised classifiers
by presenting generated adversarial DoS samples to a trained model and understanding their …

… In this work, we have discovered that deep neural networks … We investigate to which extent
adversarial attacks can be … corrupt carefully designed adversarial perturbations, rendering …

… In which adversarial attack achieve high 100% misclassification rate. A conventional IT
safety ecosystem comprises static network defences of perimeters (ie, firewalls, IDSs), the all-…

… adversarial examples (AEs) is unknown. In this article, we design a novel adversarial attack
against DL-based network … -art NIDS, Kitsune: the adversary only needs to modify less than …

… to defend against adversarial attacks in IoT networks. Our adversarial samples generation
approach has considered the attack function and underlying logic of the network flow, and …

… adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review
of the body of knowledge about adversarial attacks … taxonomy of adversarial attacks within …

… has physical access to the meter, and/or it has compromised the encryption keys used (if
any) and it has compromised any of the network elements. 2) We consider a white-box …

… smart home networks under adversarial attack. The proposed method leverages the network
… Various adversarial attack scenarios are also designed and implemented to evaluate the …

… the connection between adversarial attacks and neural network interpretability by investigating
… (Selvaraju et al., 2016), a popular deep neural network (DNN) interpretability technique. …