Search

Search Results

Results 2671-2680 of 3055 (Search time: 0.113 seconds).
Item hits:
  • Sách/Book


  • Authors: Siva Kumar, Ram Shankar (2023)

  • A robust and engaging account of the single greatest threat faced by AI and ML systems In Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats.

  • Sách/Book


  • Authors: Tari, Zahir (2023)

  • Data Exfiltration Threats and Prevention Techniques provides readers the knowledge needed to prevent and protect from malware attacks, raising awareness of the increasing number of attacks each year. Provided with a detailed description of the recent advances in data exfiltration detection methods and technologies, the authors discuss details of data breach countermeasures and attack scenarios to show how the reader may identify a potential cyber attack in the real world. Aimed at professionals and students alike, this book highlights a range of machine learning methods that can be used to detect potential data theft, identifying research gaps and the potential to make change in the future as technology continues to grow. Comprised of eight chapters, this book presents a better unde...

  • Sách/Book


  • Authors: Kanungo, Deepak K (2023)

  • Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how

  • Sách/Book


  • Authors: Roberts, Terisa (2022)

  • This book provides an overview and introduction to the application of artificial intelligence and machine learning in risk management. It will cover practical application of newer modelling techniques in risk management and explore what the opportunities are of using artificial intelligence and machine learning, as well as the risks and challenges associated with the innovation. In addition, it will explain the options to extend the model governance framework for artificial intelligence and machine learning