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Supporting AI/ML Security Workers through an Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) Framework

Year: 2022

Type: preprint

Abstract: This paper focuses on supporting AI/ML Security Workers -- professionals involved in the development and deployment of secure AI-enabled software systems. It presents AI/ML Adversarial Techniques, Tools, and Common Knowledge (AI/ML ATT&CK) framework to enable AI/ML Security Workers intuitively to explore offensive and defensive tactics.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2211.05075

Authors: Fazelnia, Mohamad, Okutan, Ahmet, Mirakhorli, Mehdi

Institution: Rochester Institute of Technology

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4308760100

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