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
- Defining Machine Learning Security
- Mitigating Risk at Training by Validating and Maintaining Datasets
- Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
- Considering the Threat Environment
- Keeping Your Network Clean
- Detecting and Analyzing Anomalies
- Dealing with Malware
- Locating Potential Fraud
- Defending Against Hackers
- Considering the Ramifications of Deepfakes
- Leveraging Machine Learning Against Hacking
- Embracing Ethical Behavior
