The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

One of the main challenges in security today is defending against malware attacks. As
trends and anecdotal evidence show, preventing these attacks, regardless of their …

As machine learning becomes widely used for automated decisions, attackers have strong
incentives to manipulate the results and models generated by machine learning algorithms …

Hyperparameters are critical in machine learning, as different hyperparameters often result
in models with significantly different performance. Hyperparameters may be deemed …

Transferability captures the ability of an attack against a machine-learning model to be
effective against a different, potentially unknown, model. Empirical evidence for …

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 …

Malicious applications pose a threat to the security of the Android platform. The growing
amount and diversity of these applications render conventional defenses largely ineffective …

Today's systems produce a rapidly exploding amount of data, and the data further derives
more data, forming a complex data propagation network that we call the data's lineage …

To cope with the increasing variability and sophistication of modern attacks, machine
learning has been widely adopted as a statistically-sound tool for malware detection …