Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning

arXiv:2403.19009v1 Announce Type: cross
Abstract: The widespread adoption of machine learning (ML) across various industries has raised sustainability concerns due to its substantial energy usage and carbon emissions. This issue becomes more pressing in adversarial ML, which focuses on enhancing model security against different network-based attacks. Implementing defenses in ML systems often necessitates additional computational resources and network security measures, exacerbating their environmental impacts. In this paper, we pioneer the first investigation into adversarial ML's carbon footprint, providing empirical evidence …

adoption adversarial arxiv attacks carbon cs.cr cs.lg defenses emissions energy industries issue machine machine learning network security sustainability