Description

Book Synopsis
Machine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions. Therefore, carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions, irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks, particularly in the malware context, and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore, to study systemic weaknesses that can improve the resilience of machine learning models.

Table of Contents
The Beginnings of Adversarial ML.- Framework for Adversarial Malware Evaluation.- Problem-Space Attacks.- Feature-Space Attacks.- Closing Remarks.




Machine Learning under Malware Attack

    Product form

    A Paperback by Raphael Labaca-Castro

    £61.74

    Includes FREE delivery

    RRP £64.99 – you save £3.25 (5%)

    Order before 4pm tomorrow for delivery by Mon 5 Oct 2026.

    1 in stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Machine Learning under Malware Attack by Raphael Labaca-Castro

      Book details

      Publisher Springer Fachmedien Wiesbaden
      Published 1 February 2023
      ISBN-13 9783658404413
      978-3658404413
      ISBN-10 3658404418
      Also in:
      Computing Machine learning Technology, Engineering & Agriculture Automotive technology and trades

      Description

      Book Synopsis
      Machine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions. Therefore, carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions, irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks, particularly in the malware context, and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore, to study systemic weaknesses that can improve the resilience of machine learning models.

      Table of Contents
      The Beginnings of Adversarial ML.- Framework for Adversarial Malware Evaluation.- Problem-Space Attacks.- Feature-Space Attacks.- Closing Remarks.




      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account