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Key benefits

Discover how hackers rely on misdirection and deep fakes to fool even the best security systems

Retain the usefulness of your data by detecting unwanted and invalid modifications

Develop application code to meet the security requirements related to machine learning

Description

Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning. As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references. The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies. This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks. By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.

Who is this book for?

Whether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.

What you will learn

Explore methods to detect and prevent illegal access to your system

Implement detection techniques when access does occur

Employ machine learning techniques to determine motivations

Mitigate hacker access once security is breached

Perform statistical measurement and behavior analysis

Repair damage to your data and applications

Use ethical data collection methods to reduce security risks

What do you get with Audiobook?

Download a zip folder containing audio files (MP3) and a supplementary PDF

Access this title in our online player

DRM FREE - Listen whenever, wherever and however you want

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Adaobi Mar 12, 2023

Machine Learning Security Principles is so much more than a book about security. It is a training manual on how to be responsible with data in a world where everyone is incorporating ML into every aspect of their business without truly understanding what ML is or how to use it effectively.ML has made mundane tasks so much more efficient and easier to process, but has in many ways has left organizations and the data they have vulnerable to hackers. John Mueller's expertise in AI, security, and programming makes him a great go-to source for understanding what ML is, learning how to secure your organization's data and make your network less vulnerable to attacks, and figuring out whether you are dealing with fraud. He even seals it all by showing you how to be ethically responsible when building your ML applications so that you're not holding on to such extremely sensitive data in the first place.This book is and informative and important read for anyone working with ML systems and emphasizes the importance of safeguarding those systems.

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Disesdi Susanna Cox Mar 16, 2023

As an industry practitioner working in the machine learning security space, I found this to be a fantastic introduction to many security challenges facing AI/ML engineers, and critically, their mitigations. The book covers not only adversarial machine learning attacks, but also non-ML driven vulnerabilities, and gives stakeholders solid advice on how to address these. I particularly appreciated advice on how to minimize threat surfaces and “avoid helping hackers,” critical information for an industry where security can sometimes be a lower priority than rapid prototyping and innovation. I would love to see future editions give even more emphasis to putting security into production, as in my experience this is something many organizations struggle with. Overall this book is a huge step forward for ML security awareness, and a must-read for anyone working on AI/ML systems in production.

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Juan Jose Apr 08, 2023

As a cybersecurity professional turned AI engineer, I have been searching for resources that combine both fields, and "Machine Learning for Security: Principles, Applications, and Techniques" has not disappointed me. This book is an excellent compendium of essential knowledge, and the authors have made it engaging and accessible to readers with varying levels of expertise.The book begins by laying a solid foundation of machine learning concepts and gradually moves to discuss their applications in the realm of cybersecurity. What truly sets this book apart is its use of real-world examples and case studies, making it easier to understand the practical aspects of implementing these techniques in diverse security scenarios. The hands-on exercises and code snippets provided throughout the book are invaluable for those looking to apply their newfound knowledge.As someone who is passionate about responsible AI, I appreciate the authors' dedication to addressing the ethical considerations of utilizing machine learning in security applications. The book thoughtfully discusses potential biases and pitfalls that may arise in these systems and offers guidance on designing transparent and ethical algorithms. This attention to detail sets the book apart from others in the field.In conclusion, "Machine Learning for Security: Principles, Applications, and Techniques" is an indispensable resource for anyone interested in the confluence of machine learning and cybersecurity. Whether you are a seasoned professional or a newcomer, this book will serve as a trusted guide, helping you navigate and excel in this rapidly evolving domain.

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Luca Massaron Feb 28, 2023

The elephant in the room is that we do talk a lot about machine learning technicalities, from model building to deploying, but the security and reliability of the solutions we create is seldom mentioned or considered anywhere. John's book, for which I have been one of the technical reviewers, is one of the few ones to illustrate and exemplify what security implies in machine learning.Using a clear language and many examples, the book approaches the topic by going from defining machine learning security to specific areas of interest such as risk mitigation in model development, adversarial machine learning attacks, anomalies, malware on systems and networks. It also touches topics related to security such as frauds, deep fakes, ethical behavior and fairness in machine learning.As a machine learning expert I found much information on the security world that I didn't know. I noticed and appreciated how the author takes great care in explaining core concepts and ideas from the basis, making it an ideal guide for everyone working in machine learning and AI and willing to approach security from its foundations. I recommend the book as a solid tool to acquire all the knowledge to rethink machine learning and AI also under the perspective of security.

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Nirmal B Feb 18, 2023

I got an opportunity to be an early reviewer of this book. I must say that it is one of the rare collections that you will find about security in ML models. It is very common that people write and talk about building ML models, however it is always rare that people talk about securing the ML model itself. I work in security domain, and ML; and I have found that because data science and ML are mostly about using open source libraries and packages, sometimes the security or threat modeling of the ML system is overlooked or bypassed. However if your data or model is corrupted, then the model will misbehave or behave as instructed by the hackers.Author has done a great job in covering security principles from different stages of ML workflow- including training data to inference (model poisoning and evasion), along with anomalies and what to look for.The only reason I gave 4 instead of 5, is because the book has tried to cover little bit more information than actually needed from ML security standpoint. Some of the sections like Network related security and AI fairness, and ethical AI are good information, but I do also feel it overloads from different directions. However if you are looking for more info the better, this could be added value too.Overall it is a great book and must read if you are building ML models and want to do it in a secure way. Think about this- if you want to put your model in production, a working model is not the suffice answer, a working and secured model is the way to go :)

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Machine Learning Security Principles: Keep data, networks, users, and applications safe from prying eyes

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