Abstract
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.
Highlights
Classification is a major research area with important applications in security and cybersecurity, including fraud detection [1]: phishing detection [2], terrorism [3] or cargo screening [4]
We first consider approaches in which learning about the adversary is performed in the operational phase, studying how to robustify generative and discriminative classifiers against attacks
These could be very demanding from a computational perspective; for those cases, we present in Section 5 an approach in which adversarial aspects are incorporated in the training phase
Summary
Classification is a major research area with important applications in security and cybersecurity, including fraud detection [1]: phishing detection [2], terrorism [3] or cargo screening [4]. Stemming from their work, the prevailing paradigm when modelling the confrontation between classification systems and adversaries has been game theory, see recent reviews [7,8] This entails well-known common knowledge hypothesis [9,10] according to which agents share information about their beliefs and preferences. We first consider approaches in which learning about the adversary is performed in the operational phase, studying how to robustify generative and discriminative classifiers against attacks In certain applications, these could be very demanding from a computational perspective; for those cases, we present in Section 5 an approach in which adversarial aspects are incorporated in the training phase.
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