Principles of Robust Learning and Inference for IoBTs
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Authors
Bastian, Nathaniel D.
Jha, Susmit
Tabuada, Paulo
Veeravalli, Venugopal
Verma, Gunjan
Issue Date
2022-12-28
Type
Books, book chapters
Language
Keywords
Adaptation models , Resilience , Computer architecture , Training , Robustness , Computational modeling , Decision Making
Alternative Title
Abstract
Chapter Abstract:
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.
Description
Citation
Nathaniel D. Bastian; Susmit Jha; Paulo Tabuada; Venugopal Veeravalli; Gunjan Verma, "Principles of Robust Learning and Inference for IoBTs," in IoT for Defense and National Security , IEEE, 2023, pp.119-131, doi: 10.1002/9781119892199.ch8.
Publisher
Wiley