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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Adversarial Machine Learning

Adversarial Machine Learning

Vorobeychik, Yevgeniy
Kantarcioglu, Murat

Paperback

Series: Synthesis Lectures on Artificial Intelligence and Machine Le

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031004523
ISBN13: 9783031004520
Publisher: Springer
Published: Aug 8 2018
Pages: 152
Weight: 0.67
Height: 0.37 Width: 7.50 Depth: 9.25
Language: English

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop.

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