Principles of Robust Learning and Inference for Internet of Battlefield Things (In Internet of Things for Defense and National Security)
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Contributing USMA Research Unit(s)
Army Cyber Institute
The Internet of Battlefield Things (IoBTs) aims at providing a pervasive, heterogeneous sensing and actuation capability to enhance command and control system autonomy and agility, information analytic capabilities against adversarial influence and control of the information battle-space; delivering intelligent, agile, and resilient decisional overmatch at significant standoff and optempo. While the traditional approaches have focused on either centralized or decentralized decision-making, with the decision structure either fixed vertical stovepipes or dynamic task organized, and the information dissemination either limited (need to know) or broadcasted (need to share), IoBTs aim at providing options across these extremes of the spectrum to provide an adaptive mission-oriented network of sensors and actuators. Thus, the discovery, composition and adaptation of available network nodes for sensing, secure information sharing, and actuation is a critical capability for IoBTs. This has motivated enabling intelligent services as core components of IoBTs to make them autonomous and to enable services necessary for effective command and control. The examples of such artificial intelligence (AI) services that need to be supported by the complex autonomic IoBTs include intelligent analytics, anomaly detection in broadly heterogeneous and varied data that may be unknown combinations of sparse and voluminous, and centralized and distributed decision-making on whether received data is trustworthy or suspect. Further, the adversarial nature of the contested environment in which IoBTs operate requires enriching the resiliency of the IoBT, such that it can be hardened against tampering and adversarial compromise, continue operating under attacks, and provide bounded guarantees of performance. The tremendous success of machine learning, in particular deep learning methods, make them a promising paradigm to develop and deploy the intelligent services in an IoBT. But these machine learning models are known to be brittle, untrustworthy, and vulnerable to adversarial attacks. These limitations have fueled our research into principles and methodologies to make machine learning models robust, resilient to adversarial attacks, uncertainty-aware, and more interpretable for human-on-the-loop decision-making.
Bastian, Nathaniel D.; Jha, Susmit; Tabuada, Paulo; Veeravalli, Venugopal; and Verma, Gunjan, "Principles of Robust Learning and Inference for Internet of Battlefield Things (In Internet of Things for Defense and National Security)" (2023). ACI Books & Book Chapters. 18.
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