IoT for Defense and National Security

11Assurance by Design for Cyber‐physical Data‐driven Systems

Satish Chikkagoudar¹, Samrat Chatterjee², Ramesh Bharadwaj¹, Auroop Ganguly³, Sastry Kompella¹, and Darlene Thorsen²

¹Information Technology Division, U.S. Naval Research Laboratory, Washington, DC, USA

²Data Sciences & Machine Intelligence Group, Pacific Northwest National Laboratory, Richland, WA, USA

³Department of Civil & Environmental Engineering, Northeastern University, Boston, MA, USA

Currently, Cyber‐Physical Data‐Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation's infrastructure, such as the power grid, transportation networks (e.g. fuel distribution, air traffic control), and DoD long‐duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system‐of‐systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all‐domain nature of the problem of assuring the design of CPDDSs requires a multi‐disciplinary perspective as outlined in this chapter.

11.1 Introduction

U.S. critical infrastructure systems increasingly rely on process automation enabled by the seamless integration of information ...