Automating Cyber Threat Detection with AI and Machine Learning
Hicham Zmaimita, Abdellah Madani, Khalid Zine-Dine · 2025
Today’s world has become increasingly digital in all areas of the daily lives of organizations and individuals. This digitalization has increased the number of users connected to the Internet. As a result, the number of cybercriminals has also increased. They are constantly improving their attack techniques by using innovative tactics. Cybersecurity experts are having to abandon traditional methods of identifying cyber threats and look for new techniques to deal with well-armed cybercriminals. However, cybersecurity professionals and researchers are now using machine learning methods to identify cyber threats and combat cybercrime. The aim of this chapter is to present a study of some of the machine learning techniques used to detect known or unknown cyber threats, such as zero-day attacks. The goal of ML-driven security systems is to increase accuracy, reduce false positives, enhance real-time threat response and minimize human intervention. We presented the various machine learning methods used to detect cyber threats (intrusions, phishing, malware, etc.), the challenges and limitations of this integration.