Review: Artificial Intelligence for Cybersecurity
Artificial Intelligence for Cybersecurity is a practical guide to how AI and machine learning are changing the way we defend digital systems. The book aims to explain how AI can help solve real cybersecurity problems. It does that well, but it’s not for everyone.
About the authors
Bojan Kolosnjaji is a principal engineer and researcher specializing in AI-driven anomaly detection and large-scale cybersecurity analytics, with a PhD from TUM.
Xiao Huang is a Stanford visiting scholar and former Fraunhofer research lead focused on adversarial ML, trusted AI, and cybersecurity applications, with a doctorate from TUM.
Peng Xu is a principal engineer working on LLMs and compiler optimization for security applications, with a research background in malware detection and vulnerability mitigation.
Apostolis Zarras is a cybersecurity academic and researcher with expertise in systems and network security, known for his work on dark web threats and protective architectures.
Inside the book
The book walks you through how big data, automation, and analytics shape modern cybersecurity. From there, it covers AI techniques like supervised and unsupervised learning, neural networks, and anomaly detection. The authors also cover common cybersecurity tools and explain how AI fits into those systems.
One of the book’s strengths is its hands-on style. Many chapters include exercises and code examples using Python. These help readers understand how AI models are built and applied to malware detection, phishing detection, behavior analysis, and more. The book also includes examples from industrial control systems and LLMs (like ChatGPT) to show where the field is going.
The authors are clearly experienced researchers, and their expertise shows. They strike a balance between explaining AI fundamentals and discussing cutting-edge topics, like adversarial machine learning and responsible AI. At the same time, they do a good job warning readers about the risks: bias, hallucinations, bad data, and unrealistic expectations.
Still, there are trade-offs. The writing can be dry and technical. If you’re looking for sharp opinions or dramatic case studies, this is not that book. Some sections read like academic lectures. In places, there’s more focus on defining terms than helping the reader make decisions in a high-pressure cybersecurity job.
Also, while the book is comprehensive, it sometimes tries to cover too much. Topics like threat intelligence, LLMs, user authentication, and risk frameworks are introduced quickly, often without much depth. Readers hoping for deep dives into specific AI systems or attack simulations might need to look elsewhere.
Who is it for?
Artificial Intelligence for Cybersecurity is not flashy, but it’s smart and useful. If you’re a student or early-career security professional, this book offers a solid walkthrough of how AI is being applied in the security world.