publication . Other literature type . Preprint . Article . 2017

Data driven exploratory attacks on black box classifiers in adversarial domains

Tegjyot Singh Sethi; Mehmed Kantardzic;
Open Access
  • Published: 22 Mar 2017
  • Publisher: Elsevier BV
Abstract
New data driven framework for simulating exploratory attacks on black box classifiers.Algorithms for simple evasion attacks, to more sophisticated reverse engineering attacks.Formal adversarial model and metrics for baseline evaluation of secure learning strategies.Experimental evaluation on 10 datasets, with linear and non-linear defender models.Experimental evaluation on the black box Google Cloud Platform classifier system. While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and...
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Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Learning, Cognitive Neuroscience, Artificial Intelligence, Computer Science Applications, Machine learning, computer.software_genre, computer, Reverse engineering, business.industry, business, White hat, Data mining, Black box (phreaking), Application domain, Data-driven, Black box, Adversarial machine learning, Cloud computing, Computer science
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