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

The book is basic in general, not for advanced readers who want to know and implement solutions for securing AI or using AI for cybersecurity. The chapter that I really liked was chapter 3. Other than that, it's all basic information.

In this digital data world, we have to keep our data, networks, user details, and application scope highly secure from prying eyes, of course! Especially concerning the DATA which faces PII and GDPR compliance, we have to take additional responsibilities and secure them, but we’re running our business in a very busy schedule building innovative data products and AIML solutions, pushing the data security aspects in the back and this is not correct.In this book, the author has provided curable prescriptions to manage machine learning projects with major dosages of medications to secure a machine learning system by guiding how to create a secure system using ML, protecting against ML-Driven Attacks, and Performing ML tasks in an ethical manner.The overall content was articulated well and lined up every chapter accurately to focus on the security principles strictly.In Part 1 - The author has started his journey in this book by discussing various ML algorithms, Identifying the ML security domains, and how to add security to ML systems. As we know ML depends heavily on clean data and the dataset is the foundation for ML stages and its implementation so, the author has considered first dataset security, defining threats and mitigating dataset modifications, and corruption aspects.The author has provided the most common attack techniques like Black Swan Theory, Evasion attacks, Model poisoning, Membership inference, and Trojan and backdoor attacks with extensive and exclusive details.In Part 2 – He helped us to create a secure system using ML, where he geared up with how to consider the threat environment in terms of business threats, and social threats. His special advice on how to keep our network clean with classical examples by creating real-time defenses and using supervised learning examples and developing predictive defenses is a classic piece of work.“Detecting and Analysing Anomalies” is a major topic for every Machine Learning engineer and a must-read topic. And of course, in Dealing with Malware - Defining malware and how to generate malware detection features and classifying them are special packs and certainly we can certainly use them in every ML project.If security comes into the picture, we must certainly discuss “Fraud Detecting”, especially in the ML domain. The author has given a clear path for readers to understand the types of fraud, and how to identify the fraud sources, and demonstrated the fraud detection application as an example.In Parts 3 & 4 – the author takes us to a detailed study of “Protecting against ML-Driven Attacks” with experience security issues that rely on traditional methods that are modified to meet the demands of the ML environment with Deepfakes and Leveraging Machine Learning for Hacking are astonishing topics and rare topics in any other ML books.Performing ML Tasks in an Ethical Manner is a mandatory part of all ML projects, and every ML Engineer must be aware of this because all the developers must now ensure that data is collected ethically, cleaned properly, and used correctly in a transparent manner and make sure ML inherently more secure.Overall, the author has provided the complete path to implementing the secured ML system for ML engineers. I can give 4.0/5.0 for this. Certainly, a special effort from the author is much appreciated.- Shanthababu PandianArtificial Intelligence and Analytics | Cloud Data and ML Architect | Scrum MasterNational and International Speaker | Blogger

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.

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.

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.