  The last decades have been characterized by unprecedented technological
advances, many of them powered by modern technologies such as Artificial
Intelligence (AI) and Machine Learning (ML). The world has become more
digitally connected than ever, but we face major challenges. One of the most
significant is cybercrime, which has emerged as a global threat to governments,
businesses, and civil societies. The pervasiveness of digital technologies
combined with a constantly shifting technological foundation has created a
complex and powerful playground for cybercriminals, which triggered a surge in
demand for intelligent threat detection systems based on machine and deep
learning. This paper investigates AI-based cyber threat detection to protect
our modern digital ecosystems. The primary focus is on evaluating ML-based
classifiers and ensembles for anomaly-based malware detection and network
intrusion detection and how to integrate those models in the context of network
security, mobile security, and IoT security. The discussion highlights the
challenges when deploying and integrating AI-enabled cybersecurity solutions
into existing enterprise systems and IT infrastructures, including options to
overcome those challenges. Finally, the paper provides future research
directions to further increase the security and resilience of our modern
digital industries, infrastructures, and ecosystems.
