Data recovery via covert cognizance for unattended operational resilience Article

Sundaram, A, Abdel-Khalik, HS, Roberson, D et al. (2022). Data recovery via covert cognizance for unattended operational resilience . 151 10.1016/j.pnucene.2022.104317

cited authors

  • Sundaram, A; Abdel-Khalik, HS; Roberson, D; El Hariri, M


  • One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. To meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

publication date

  • September 1, 2022

Digital Object Identifier (DOI)


  • 151