Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Perspectives on Adversarial Classification

Version 1 : Received: 6 September 2020 / Approved: 8 September 2020 / Online: 8 September 2020 (10:25:28 CEST)

A peer-reviewed article of this Preprint also exists.

Rios Insua, D.; Naveiro, R.; Gallego, V. Perspectives on Adversarial Classification. Mathematics 2020, 8, 1957. Rios Insua, D.; Naveiro, R.; Gallego, V. Perspectives on Adversarial Classification. Mathematics 2020, 8, 1957.

Journal reference: Mathematics 2020, 8, 1957
DOI: 10.3390/math8111957

Abstract

Adversarial Classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). Most approaches to AC so far have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on Adversarial Risk Analysis.

Keywords

Classification; Adversarial Machine Learning; Security; Robustness; Adversarial Risk Analysis

Subject

MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics

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