… a machine learning system M and input sample C which we call a clean example. Let’s assume
that sample C is correctly classified by the machine learning … We found that our approach …

… Finally, we explored approaches and challenges for privacypreserving learning,
including differential privacy, exploratory and causative privacy attacks, and randomization. …

… Papers in this special issue provide an initial taxonomy of adversarial attacks and a sampling
of approaches used by adversaries to defeat current methods and by defenders to create …

… on adversarial attacks against security solutions based on machine learning techniques and
… attacks), showing that additional research is needed for this approach to be practical in real-…

… Figure 1 illustrates an example of adversarial attacks against a trained classifier. On the
left, we show a normal machine learning task of classifying samples into two classes, and the …

… of machine learning approaches in adversarial settings and to develop techniques to make
learning robust to adversarial … an overview of standard machine learning approaches, with a …

… that targets Machine Learning (ML) and Deep Learning (DL) … are termed in literature as
Adversarial Machine Learning (AML) [… a new approach to generate adversarial examples. The …

… Indeed, unlike the overwhelming majority of works that study the behavior of NIDS in an
adversarial environment and focus on deep learning approaches, this paper focuses on shallow …

… machine learning approaches are being widely employed [64]. Malware detection follows a
similar approach… A downside of using machine learning techniques to perform classifications …

… of secure machine learning; more specifically, it looks at learning mechanisms in adversarial
… book, we present a systematic approach for identifying and analyzing threats to a machine …