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

Deep neural networks obtain state-of-the-art performance on a series of tasks. However,
they are easily fooled by adding a small adversarial perturbation to the input. The …

Making classifiers robust to adversarial examples is challenging. Thus, many works tackle
the seemingly easier task of detecting perturbed inputs. We show a barrier towards this goal …

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion
detection or Malware classification. Yet, these models are vulnerable to a class of malicious …

Cyber-security is the practice of protecting computing systems and networks from digital
attacks, which are a rising concern in the Information Age. With the growing pace at which …

Despite the impressive performances reported by deep neural networks in different
application domains, they remain largely vulnerable to adversarial examples, ie, input …

While the literature on security attacks and defenses of Machine Learning (ML) systems
mostly focuses on unrealistic adversarial examples, recent research has raised concern …

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 …

The increasing abundance of large high-quality datasets, combined with significant
technical advances over the last several decades have made machine learning into a major …

Abstract Machine learning models are known to lack robustness against inputs crafted by an
adversary. Such adversarial examples can, for instance, be derived from regular inputs by …