Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

A traditional centralized method of training AI models has been put to the test by the
emergence of data stores and public privacy concerns. To overcome these issues, the …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

Deep learning (DL) can provide critical infrastructure operators with valuable insights and
predictive capabilities to help them make more informed decisions, improving system's …

Many safety-critical applications of machine learning, such as fraud or abuse detection, use
data in tabular domains. Adversarial examples can be particularly damaging for these …

Artificial intelligence (AI) systems are becoming critical components of today's IT landscapes.
Their resilience against attacks and other environmental influences needs to be ensured just …

Today, a broad range of items, ranging from smartphones to smart cars are connected
together via the Internet, also known as the Internet of Things (IoT). The IoT is powered by …

Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for
financial institutions. In recent years, machine learning-based transaction monitoring …

Given their generalization capabilities, deep learning algorithms may represent a powerful
weapon in the arsenal of antivirus developers. Nevertheless, recent works in different …

According to Cybersecurity Ventures, the damage related to cybercrime is projected to reach
$6 trillion annually by 2021. The majority of the cyberattacks are directed at financial …