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 …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Abstract Machine learning systems trained on user-provided data are susceptible to data
poisoning attacks, whereby malicious users inject false training data with the aim of …

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 …

As machine learning becomes increasingly incorporated within high impact decision
ecosystems, there is a growing need to understand the long-term behaviors of deployed ML …

Machine learning is one of the most prevailing techniques in computer science, and it has
been widely applied in image processing, natural language processing, pattern recognition …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Recent results suggest that attacks against supervised machine learning systems are quite
effective, while defenses are easily bypassed by new attacks. However, the specifications for …

In security-sensitive applications, the success of machine learning depends on a thorough
vetting of their resistance to adversarial data. In one pertinent, well-motivated attack …