Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

Abstract Machine learning and Artificial Intelligence (AI) already support human decision-
making and complement professional roles, and are expected in the future to be sufficiently …

Machine Learning-based Intrusion Detection Systems have been proven to be very effective
in the protection of IoT Networks. However, the expansion of Adversarial Machine Learning …

A myriad of recent literary works have leveraged generative adversarial networks (GANs) to
generate unseen evasion samples. The purpose is to annex the generated data with the …

In the regulation of seawater quality, it is crucial to understand the interactions between
parameters and the time-lagged effects. This paper focuses on the problem of how to obtain …

Adversarial evasions are modern threats to Machine Learning (ML) based applications. Due
to the vulnerabilities in the classic ML inference systems, botnet detectors are equally likely …

A myriad of recent literary works has leveraged generative adversarial networks (GANs) to
generate unseen evasion samples. The purpose is to annex the generated data with the …

Data has proved to be the most valuable asset in a modern world of rapidly advancing
technologies. Companies are trying to maximise their profits by getting valuable insights …