This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …

A growing number of individuals and organizations are turning to machine learning (ML)
and deep learning (DL) to analyze massive amounts of data and produce actionable …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …

The internet of things (IoT) faces tremendous security challenges. Machine learning models
can be used to tackle the growing number of cyber-attack variations targeting IoT systems …

Adversarial attacks pose a major threat to machine learning and to the systems that rely on
it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading …

Learning with adversarial robustness has been a challenge in contemporary machine
learning, and recent years have witnessed increasing attention on robust decision trees and …

Decision trees are a popular choice of explainable model, but just like neural networks, they
suffer from adversarial examples. Existing algorithms for fitting decision trees robust against …

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 …