Perspectives on Adversarial Classification
Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settin...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/7539 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/7539 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.056 004.492 Classification adversarial machine learning security robustness adversarial risk analysis Seguridad informática Informática (Informática) Matemáticas (Matemáticas) 1203.17 Informática 12 Matemáticas |
| Sumario: | Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC 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. |
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