No detailed description available for "Machine Learning Security Principles".
Sprache
Verlagsort
Basel/Berlin/Boston
Großbritannien
Zielgruppe
Editions-Typ
Produkt-Hinweis
Dateigröße
ISBN-13
978-1-80461-540-9 (9781804615409)
Schweitzer Klassifikation
Mueller John Paul:
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.Stephens Rod:
Rod Stephens has been a software developer, consultant, instructor, and author. He has written more than 30 books and 250 magazine articles covering such topics as three-dimensional graphics, algorithms, database design, software engineering, interview puzzles, C#, and Visual Basic. Rod's popular C# Helper and VB Helper websites receive millions of hits per year and contain thousands of tips, tricks, and example programs for C# and Visual Basic developers.
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 and Incorporating Ethical Behavior