Rafał Kozik

TL;DR: This work leverages the flexibility of cloud-based architectures, together with the recent advancements in the area of large-scale machine learning for shifting the more computationally-expensive and storage-demanding operations to the cloud in order to benefit of edge computing capabilities only for effectively performing traffic classification based on sophisticated Extreme Learning Machines models that are pre-built over the cloud.

TL;DR: This paper evaluates the possibility of deteriorating the performance of a well-optimised intrusion detection algorithm at test time by crafting adversarial attacks with the four of the recently proposed methods and then offers a way to detect those attacks.

TL;DR: In this paper, the palmprint and knuckles feature extraction methods for mobile contactless biometrics are proposed for unrestricted access control for mobile devices, and the major contribution of this paper is palmprint feature extraction method dedicated for the mobile contact-less biometric.

TL;DR: An ensemble method that leverages deep models such as the Deep Neural Network and Long Short-Term Memory and a meta-classifier and a stacking ensemble learning approach following the principle of stacked generalization is presented.

TL;DR: This work presents a hybrid and distributed simulation platform for cyber-security analysis of large-scale critical infrastructure systems that enables testers to assemble complex and distributed experimental scenarios in the cloud by integrating different simulated environments, on which perform sophisticated vulnerability analysis.