“Artificial Intelligence for Cyber Security stands as a significant contribution to the rapidly evolving intersection of AI and cybersecurity. The authors have successfully created a comprehensive resource that fills the gap between theoretical AI concepts and practical security implementations. The book’s strength lies in its methodical approach to complex topics in security contexts like malware detection, network analysis, and threat intelligence.

The content progresses logically from basics to advanced applications, suiting both security professionals entering AI and seasoned experts. Python code examples and real-world use cases add practical value, but could be more extensive.

The book excels in traditional machine learning for security, but has limited coverage of emerging technologies like transformers and LLMs. The practical implementations, while useful, could benefit from more comprehensive end-to-end examples and detailed performance metrics. That stated, AI is an area that is in constant flux, so it is understandable in the approach in this first edition.”

Lester Nichols, Director Security Architecture/VP Sr. Lead Cybersecurity Architect IT Sec & Compliance Integration at JPMorgan Chase & Co., and Author of Cybersecurity Architect’s Handbook

Bojan Kolosnjaji is a researcher working at the intersection of artificial intelligence (AI) and cybersecurity. He has obtained his master’s and PhD degrees in computer science from the Technical University of Munich (TUM), where he conducted research in anomaly detection methods in constrained environments. Bojan’s academic work deals with anomaly detection problems in multiple cybersecurity-relevant scenarios, and the design of AI-based solutions to these problems. Bojan is currently working as a principal engineer in cybersecurity sciences and analytics, helping various cybersecurity teams deal with large-scale data, adopt AI practices and solutions, and understand security challenges in AI systems.

Xiao Huang holds a doctorate in computer science from TUM. He is also a visiting scholar at Stanford University. His main research interests include adversarial machine learning (ML), reinforcement learning, anomaly detection, trusted AI, and AI applications in cybersecurity. Huang has published several top-tier conference and journal papers with over a thousand citations in both the ML and security domains. He led the ML research group at Fraunhofer AISEC Institute in Munich and also worked as a research scientist at Bosch Center for AI. He managed a data scientist team that designed and developed ML systems to tackle different cybersecurity problems.

Peng Xu has focused on AI for system security, large language model (LLM) security, graph neural networks, program analysis, compiler design, optimization, and cybersecurity. He completed his master’s at the Chinese Academy of Science in 2013 and pursued a PhD in IT security at TUM from 2015 to 2019. He is currently awaiting his dissertation defense. Peng’s research topics include malware detection, private computation, and software vulnerability mitigation using compiler-based approaches. Peng is currently working as a principal engineer in compiler optimization and programming LLMs, especially on the topics of using LLMs to generate code blocks to detect malicious code as well as bug localization.

Apostolis Zarras is a cybersecurity researcher with a rich academic background. He has served as a faculty member at both Delft University of Technology and Maastricht University. Dr. Zarras earned his PhD in IT security from Ruhr-University Bochum, where he honed his expertise in systems, networks, and web security. His research is driven by a passion for developing innovative security paradigms, architectures, and software that fortify ICT and IoT systems. Beyond his technical contributions, Dr. Zarras delves into the dark web and its underground markets, uncovering and combating malicious activities to bolster global cybersecurity. His work is dedicated to advancing IT security and protecting users and systems from emerging cyber threats.