Principles of Robust Learning and Inference for IoBTs

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

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationIoT for Defense and National Security
Number of pages13
ISBN (Electronic)9781119892199
ISBN (Print)9781119892144
StatePublished - Jan 1 2022

ASJC Scopus subject areas

  • General Social Sciences
  • General Engineering


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