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https://personal.utdallas.edu/~muratk/publications/kdd2012.pdf
We develop a learning strategy that solves a general convex optimization problem where the strength of the constraints is tied to the strength of attacks. We derive optimal support vector machine learning models against an adversary whose attack strategy is de ned under a …
https://dl.acm.org/citation.cfm?id=2339697
Adversarial support vector machine learning. Pages 1059–1067. Previous Chapter Next Chapter. ABSTRACT. Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack ...Cited by: 71
https://en.wikipedia.org/wiki/Adversarial_machine_learning
Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique can be applied for a variety of reasons, the most common being to attack or cause a malfunction in standard machine learning models.
https://www.sciencedirect.com/science/article/pii/S0925231219306150
This work aims at mitigating consequences of adversarial machine learning attacks and at developing counter-adversarial strategies. The majority of machine learning techniques, in particular support vector machines (SVMs), make a prediction based on the assumption that test data is sampled from the same distribution as training data.Cited by: 1
https://pralab.diee.unica.it/en/AdversarialMachineLearning
Adversarial Learning is a novel research area that lies at the intersection of machine learning and computer security. It aims at gaining a deeper understanding of the security properties of current machine learning algorithms against carefully targeted attacks, and at developing suitable countermeasures for the design of more secure learning ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.674.7423
adversarial support vector machine learning attack model restrained attack model active adversary optimal solution wide range optimal svm attack parameter actual attack per-mits arbitrary data corruption resilient svm real data set realistic attack hinge loss credit card fraud detection face opti-mal svm learning strategy free-range attack ...
http://pralab.diee.unica.it/sites/default/files/biggio14-neurocomp.pdf
data manipulation is thus an important, additional requirement for machine learning algorithms to successfully operate in adversarial settings. In this work, we evaluate the security of Support Vector Machines (SVMs) to well-crafted, adversarial label noise attacks.
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