Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents

2020 ◽  
Author(s):  
Asunur Cezar ◽  
Srinivasan Raghunathan ◽  
Sumit Sarkar
Keyword(s):  
Adversarial Classification

Adversarial Classification When Even Good Types Can Fake

2007 ◽  
Author(s):  
Asunur Cezar ◽  
Srinivasan Raghunathan ◽  
Sumit Sarkar
Keyword(s):  
Adversarial Classification

scholarly journals A Fundamental Performance Limitation for Adversarial Classification

2020 ◽  
Vol 4 (1) ◽  
pp. 169-174 ◽  
Author(s):  
Abed Al Rahman Al Makdah ◽  
Vaibhav Katewa ◽  
Fabio Pasqualetti
Keyword(s):  
Adversarial Classification ◽  
Performance Limitation

Adversarial classification using signaling games with an application to phishing detection

2016 ◽  
Vol 31 (1) ◽  
pp. 92-133 ◽  
Author(s):  
Nicolas Figueroa ◽  
Gastón L’Huillier ◽  
Richard Weber
Keyword(s):  
Signaling Games ◽  
Adversarial Classification ◽  
Phishing Detection

scholarly journals Perspectives on Adversarial Classification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1957
Author(s):  
David Rios Insua ◽  
Roi Naveiro ◽  
Victor Gallego
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
Common Knowledge ◽  
Adversarial Classification ◽  
Game Theoretic ◽  
Classical Game

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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