Artificial Intelligence for Cybersecurity: Develop AI approaches to solve cybersecurity problems in your organization

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Key benefits

Familiarize yourself with AI methods and approaches and see how they fit into cybersecurity

Learn how to design solutions in cybersecurity that include AI as a key feature

Acquire practical AI skills using step-by-step exercises and code examples

Purchase of the print or Kindle book includes a free PDF eBook

Description

Artificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.

Who is this book for?

This book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.

What you will learn

Recognize AI as a powerful tool for intelligence analysis of cybersecurity data

Explore all the components and workflow of an AI solution

Find out how to design an AI-based solution for cybersecurity

Discover how to test various AI-based cybersecurity solutions

Evaluate your AI solution and describe its advantages to your organization

Avoid common pitfalls and difficulties when implementing AI solutions

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Tiny Nov 01, 2024

cross the software industry, we have all become part of the AI/ML trend, looking for options, incorporating solutions, and finding the next best path to accelerate value. “Artificial Intelligence for Cybersecurity” (Packt, 2024) by Bojan Kolosnjaji, Huang Xiao, Peng Xu, and Apostolis Zarras expands AI solutions into the cybersecurity field. Cybersecurity, due to large amounts of data, difficult problems, and changing attackers is probably one field where AI solutions can create the largest impact, soonest. The book divides five sections into 19 short chapters across 358 pages. The content includes historical premises, mathematical explanations, code samples, practical examples, and charts for quick reference. If you work anywhere in cybersecurity, I strongly recommend this book as an initial learning tool and a deskside reference.The first section addresses an initial historical basis and baseline for some AI/ML techniques while the second expands into how those areas fit today. One starts by recognizing the impact of Big Data, and how automated workflows adjust for increasing information. This expands to recognizing where SIEM and SOAR tools already use some ML functions in accomplishing analysis. The authors build that initial understanding into detailing how different ML solutions work with a full statistical review, demonstrating the math for different problem sets. The second section concludes with examples of using existing libraries to build malware detection and visualizing network traffic plans.With the initial basis summarized, the third section expands into the longest single section with seven chapters. Each chapter examines a different cybersecurity problem. The examined problems include user analysis, spam detection, user authentication, threat intelligence, and an excellent chapter on Industrial Control Systems (ICS). The section also thoroughly looks at LLM models suggesting how these can improve security and how to design attacks against these systems. As a long time researcher, the ICS chapter was awesome, looking at DarkEnergy, Stuxnet, and Colonial Pipeline attacks. This demonstrated how one can attack ICS from a frontal attack, lateral movement into subordinate control systems, and using ransomware to shut down the administrative frameworks that govern ICS.The last functional section returns the reader to one of my favorite topics, metrics. Starting with how to identify data quality, a thorough mathematical examination of correlation, causation, and bias helps readers understand where one can adjust ML solutions. This builds to how one creates observability to obtain feedback, and then moves to another favorite topic, Adversarial Machine Learning (AML). AML is the process of setting one ML solution to counter another, leading to progressively more capable devices. For example, designing one system to make artistic pictures, and another to evaluate as real or fake, than resetting the learning models. The final two chapters look at some ways ahead, and some of the ethical concerns associated with AI.My one gripe is I could use some deeper exploration on some of the issues. The short chapters made me feel as if I was just getting into the topic when it was over. The examples helped, and understanding the math beyond ML operations is always significant. In a couple of places, I could spend days looking at ML solutions to identify and access problems, or the inclusion of AI/ML into existing SOAR solutions. Despite the brief coverage, every element was still covered in sufficient detail to meet the book’s intent.Overall, this was a great book for any cybersecurity expert. “Artificial Intelligence for Cybersecurity” (Packt, 2024) delivers an effective summary of the past several year’s growth in the industry, detailed baseline understanding, and practical models for the reader to test. 19 short chapters, five effective sections, numerous examples, and an exhaustive related reading list provides the cybersecurity expert with everything they might be seeking about AI/ML solutions. Recommend this book as a desk reference for anyone involved with modern cybersecurity. Read more

Amazon Verified review

Michael Jan 10, 2025

This book was a great asset!I highly recommend to anyone trying to get ahead of the curve. AI is poised to be the most eventful advancement in human history. In my opinion it is the only thing worth learning right now. Read more

Sri S. Dec 06, 2024

For any cybersecurity detection engineer who wants to improve/expand their available set of tools and techniques, “AI for Cybersecurity” is a wonderful resource. It provides a quick introduction to ML basics and to several aspects in cybersecurity. Great hands-on book for those already familiar with Python. The authors have done an excellent job in explaining this in 5 logical parts, each focussing on specific aspects of security. Read more

Timo Prime Feb 05, 2025

This book is poorly written. Lots of long, run-on sentences filled with excessive commas and AI buzzwords. It almost reads like AI-generated slop. Not recommended. Read more

Artificial Intelligence for Cybersecurity: Develop AI approaches to solve cybersecurity problems in your organization

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