Abstract Machine learning (ML) methods have demonstrated impressive performance in
many application fields such as autopilot, facial recognition, and spam detection …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

In recent years, machine learning algorithms, and more specifically deep learning
algorithms, have been widely used in many fields, including cyber security. However …

The majority of machine learning methodologies operate with the assumption that their
environment is benign. However, this assumption does not always hold, as it is often …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Machine learning (ML) systems have been increasingly applied in web security applications
such as spammer detection, malware detection and fraud detection. These applications …

Written by leading researchers, this complete introduction brings together all the theory and
tools needed for building robust machine learning in adversarial environments. Discover …

Machine learning is widely used in security-sensitive settings like spam and malware
detection, although it has been shown that malicious data can be carefully modified at test …

Machine Learning (ML) algorithms, specifically supervised learning, are widely used in
modern real-world applications, which utilize Computational Intelligence (CI) as their core …

Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms
are being used in diverse domains where security is a concern, such as, automotive …