Principles of Robust Learning and Inference for IoBTs Article Swipe

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Nathaniel D. Bastian , Susmit Jha , Paulo Tabuada , Venugopal V. Veeravalli , Gunjan Verma ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1002/9781119892199.ch8

The Internet of Battlefield Things (IoBTs) operate in an adversarial rapidly-evolving environment, necessitating fast, robust and resilient decision-making. The success of machine learning, in particular deep learning methods, can improve the performance and effectiveness of IoBTs, but these models are known to be brittle, untrustworthy, and vulnerable. In this chapter, we discuss the principles and methodologies to make machine learning models robust, resilient to adversarial attacks, and more interpretable for human-on-the-loop decision-making. We also identify the key challenges in developing trustworthy machine learning for IoBTs.

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Type: other
Language: en
Landing Page: https://doi.org/10.1002/9781119892199.ch8
PDF: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/9781119892199.ch8
OA Status: gold
Cited By: 2
References: 48
Related Works: 10
OpenAlex ID: https://openalex.org/W4313610859

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