Malware has been one of the most damaging threats to computers that span across multiple
operating systems and various file formats. To defend against ever-increasing and ever …

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 …

Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …

Recent advances in generative models for language have enabled the creation of
convincing synthetic text or deepfake text. Prior work has demonstrated the potential for …

Adversarial examples are known as carefully perturbed images fooling image classifiers. We
propose a geometric framework to generate adversarial examples in one of the most …

Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove
vulnerable to adversarial examples. Through these, attackers that may be oblivious to the …

Most current approaches for protecting privacy in machine learning (ML) assume that
models exist in a vacuum, when in reality, ML models are part of larger systems that include …