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

John Paul Mueller

ISBN 10: 1804618853 / ISBN 13: 9781804618851

Editore: Packt Publishing Limited, 2022

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This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.

Codice articolo C9781804618851

Dati bibliografici

• Titolo: Machine Learning Security Principles: Keep data, networks, users, and applications safe from prying eyes
• Editore: Packt Publishing Limited
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1804618853
• ISBN 13: 9781804618851
• Rilegatura: Paperback / softback
• Condizione: New
• Peso: 100 grammi

Descrizione articolo

Riassunto

Thwart hackers by preventing, detecting, and misdirecting attempted access before the hacker can obtain credentials, engage in fraud, dig in deep, modify data, corrupt users, and otherwise completely ruin your day.

Key Features

• Discover how hackers now rely on deep fakes to fool even the best security system.
• Retain the usefulness of your data by detecting unwanted and invalid modifications.
• Develop application code that addresses a number of security needs related to machine learning.

Book Description

Machine learning is the most important new technology for getting more out of data today. It can reveal patterns that aren't obvious, for example, but it requires data, lots of it. Data gathering isn't just about data. It affects users and requires the use of applications to clean, manipulate, and analyze the data. Obtaining data in an ethical manner is important because the very act behaving ethically reduces the security risk associated with data. However, hackers don't necessarily target users and their data. Perhaps they're interested in your organization's trade secrets or committing fraud. They might simply be interested in lurking in the background and committing mischief. So, just keeping your data secure as a means of protecting your machine learning investment isn't enough. You need to do more.

This book helps you get the big picture from a machine learning perspective using all the latest research available on methods that hackers use to break into your system. It's about the whole system, not just your application. You discover techniques that help you gather data ethically and keep it safe, while also preventing all sorts of illegal access method from even occurring. In fact, you use machine learning as a tool to keep hackers at bay and discover their true intent for your organization.

What you will learn

• Learn methods to prevent illegal access to your system
• Discover detection techniques when access does occur
• Employ machine learning techniques to determine motivations
• Mitigate hacker access using a variety of methods
• Repair damage to your data and applications
• Use ethical data collection methods to reduce security risks

Who This Book Is For

Data scientists and computer scientists who develop machine learning applications of any type including students will find this book helpful. Machine Learning and Security researchers and scientists that are involved in some type of theoretical activity that benefits from machine learning will benefit from this book. Having working knowledge of Python Programming is required to get the best from this book.

Table of Contents

1. Defining Machine Learning Security
2. Mitigating Risk at Training by Validating and Maintaining Datasets
3. Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
4. Considering the Threat Environment
5. Keeping Your Network Clean
6. Detecting and Analyzing Anomalies
7. Dealing with Malware
8. Locating Potential Fraud
9. Defending Against Hackers
10. Considering the Ramifications of Deepfakes
11. Leveraging Machine Learning Against Hacking
12. Embracing Ethical Behavior

Informazioni sull?autore

John Paul Mueller is a seasoned author and technical editor. He has writing in his blood, having produced 121 books and more than 600 articles to date. The topics range from networking to artificial intelligence and from database management to heads-down programming. Some of his current books include discussions of data science, machine learning, and algorithms. He also writes about computer languages such as C++, C#, and Python. His technical editing skills have helped more than 70 authors refine the content of their manuscripts. John has provided technical editing services to a variety of magazines, performed various kinds of consulting, and he writes certification exams.