Advancing Cyber Defense with Artificial Intelligence
Cristian Randieri, Claudio Tomazzoli · 2025
The cybersecurity attack landscape requires continuously developing innovations and methodologies alongside the most widespread and consolidated technologies. In complex and evolving scenarios such as malware mitigation, ransomware detection, botnet identification, and adaptive countermeasures, integrating artificial intelligence (AI) with cybersecurity aims to improve the ability to respond to these attacks by offering new solutions for increased resilience. This chapter begins with an introduction of the current state of the art in terms of enabling methodologies and techniques, as well as a discussion of different techniques used in the complex AI paradigm, such as supervised learning, unsupervised learning, reinforcement learning, natural language processing (NLP), and deep learning (DL) in the cybersecurity domain. In this regard, we discuss, in terms of application scenarios, the role of AI in addressing contemporary threats such as ransomware, phishing, malware, and botnets by reviewing relevant case studies and research findings to highlight the superior performance of AI compared to traditional methods. Our study concludes by highlighting AI’s great innovative and transformative potential in cybersecurity despite current challenges and suggesting possible avenues for future research.