Four cybersecurity researchers with deep technical backgrounds walk through how AI methods can be applied to genuine security problems — malware detection, intrusion detection, threat intelligence, and more. Artificial Intelligence for Cybersecurity stays close to real organizational use cases rather than theoretical possibility.
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In StockCybersecurity is one of the areas where AI has moved from interesting possibility to genuine operational necessity, and Artificial Intelligence for Cybersecurity is written by researchers who work at that intersection professionally. The four authors cover how machine learning and AI techniques apply to a range of concrete security problems: detecting malware and network intrusions, analyzing threat intelligence at scale, identifying anomalous behavior in systems, and automating responses to threats that move faster than human analysts can track. Throughout the book, the focus stays on practical application within real organizational contexts rather than abstract demonstration of AI capability — a distinction that matters when security practitioners need to make decisions about what to actually deploy and how. The technical depth is substantial, making the book most valuable for security professionals with some background in both security operations and machine learning, rather than readers looking for a gentle introduction to either field independently. At 358 pages, it covers a solid range of security use cases with enough technical specificity to inform real implementation decisions. For cybersecurity teams looking to understand and integrate AI into their defensive practices, this offers a credible, practitioner-oriented resource.
Author:
Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis Zarras
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