Adversarial Machine Learning

Clicks: 2
ID: 287871
2019
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Ranked #250 of 1,244 articles by views in ADVANCES IN ARCHAEOLOGICAL PRACTICE

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Abstract
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
Reference Key
persistent_1761420008_68fd22e8c462b Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J. D. Tygar
Journal ADVANCES IN ARCHAEOLOGICAL PRACTICE
Year 2019
DOI
10.1017/9781107338548
URL
https://www.cambridge.org/core/books/adversarial-machine-learning/C42A9D49CBC626DF7B8E54E72974AA3B
Keywords Keywords not found

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