TECHNOLOGY AREA(S): Battlespace, Electronics, Sensors

ACQUISITION PROGRAM: PMS 450W, VIRGINIA Class Program Office

OBJECTIVE: Develop computational data analyzer tool sets that processes machinery condition information evaluating patterns that can cause cyber security vulnerabilities and to reduce total ownership costs as well as enabling cyber secure management of machinery monitoring that minimizes risk to information for maintenance actions.

DESCRIPTION: The U.S. Navy is currently developing condition based monitoring concepts and technologies to provide diagnostic and prognostic capabilities using Machine Learning (ML) techniques. Both industry and the U.S. Department of Defense have developed several ongoing research areas, which include characterization of vulnerabilities, isolating and explaining causes of uncertainty, uncertainty-aware learning, etc. However, to the best of our knowledge, the applications of ML to formulate maintenance decisions on condition-based maintenance plus (CBM+) platform have not yet been explored. Additionally, while existing strategies can be adopted to minimize vulnerabilities and improve cyber resiliency of CBM+ systems, stable versions of learning problems are not well understood due to the nature of CBM+ data. These concepts and technologies will enhance fleet performance and readiness through improved equipment availability, reliability, operation, and maintenance over their entire lifecycle. Advancement in low-power embedded sensors, microcontrollers, and wireless technologies has fostered development of new sensor nodes and computational processes that enable use of CBM+ strategies. These CBM+ platforms represent a growing class of cyber-physical systems (CPS) that are being considered for integration on existing and future Navy vessels. While providing in situ monitoring capabilities and allowing maintenance practices to be more efficient through better informed reliability centered maintenance (RCM) analyses, these sensor nodes have the potential to serve as targets for cybersecurity attacks or be susceptible to corruption through accidental or malicious events.

As discussed above, existing strategies can be adopted to minimize vulnerabilities; however, it is impossible to eliminate these risks. Consequently, the Navy is interested in concepts and methods for improving cyber resiliency of condition based monitoring systems (CBMS) that can monitor the Hull, Mechanical & Electrical (HM&E) equipment used to sustain operation and performance of the Fleet. From a traditional perspective, a variety of techniques can be used to improve the cyber resilience of computing systems and networks. These techniques include, but are not limited to, diversity and heterogeneity of system elements, distributed allocation of resources, component redundancy, configuration hopping, and data continuity checking. Virtual models can also be used to provide a digital twin of HM&E equipment for Condition Based Maintenance (CBM) purposes and have dual application for detecting and responding to cyberattacks.

Many CBM+ applications are constrained by the requirement to operate under power, or computing restrictions when deployed on wireless hardware that operates off an internal battery. In these cases, the cybersecurity layer must be implemented effectively while minimizing impact on power consumption and overall lifespan of the embedded CBM+ sensor node. A successful technology development and transition will result in a secure CBM+ sensor node that can minimize human intervention and reduce the number of machinery overhauls, shorten time spent in depot for repairs, and optimize maintenance logistics by at least 50%.

PHASE I: Define and develop a concept for enhancing the cyber resilience of embedded sensing hardware and software used in CBM and prognostic applications following NIST and ISO/IEC 27001 ad 27002 standards. Evaluate the type and source of vulnerabilities that could be exploited for a wireless network of condition monitoring sensor nodes, considering both accidental and malicious events. The framework will need to be flexible and extensible across a set of hardware systems, with a proposed design for the hardware and software architectures that will be incorporated into the CBMS for enhanced cyber resiliency.� The design should include a summary of the computing and power requirements for incorporating the cybersecurity layer to the CBMS. The feasibility of the concept will be established through modelling and simulation. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop a prototype for evaluation using either Java or C++ on CentOS platform. Design the prototype to provide a hardware/software layer that can be added to a CBMS sensor network. Demonstrate the design performance through modeling and physical testing over a range of scenarios devised to test the network vulnerability with and without the cyber resilient layer in place. Use evaluation results to refine the prototype into an initial design that can be used in relevant and/or operational environment settings, and to support mission requirements in the cyber domain, which ensures the confidentiality, integrity, and availability of data. Develop a Phase III plan to transition the technology to a system that can be acquired by the Navy.

PHASE III DUAL USE APPLICATIONS: Support Navy system integration of the cybersecurity framework, hardware and software, employing any lessons learned from the Phase II evaluation. Incorporate the cyber resiliency techniques into existing CBMS and will consist of validation testing and demonstration on a representative HM&E system.

The software techniques using ML and hardware developed in this STTR effort could support any deployed CBMS or health monitoring system used for industry, infrastructure, energy, health care, or other applications where cyberattacks may be expected to interfere with the integrity or availability of data and analysis from embedded cyber-physical systems.

REFERENCES:

1. �Condition Based Maintenance Plus DoD Guidebook.� Department of Defense, May 2008.
https://www.dau.mil/guidebooks/Shared%20Documents%20HTML/Condition%20Based%20Maintenance%20Plus%20(CBM+)%20Guidebook.aspx

2. Farinholt, K., Chaudhry, A., Kim, M., Thompson, E., Hipwell, N., Meekins, R., Adams, S., Beling, P. and Polter, S. �Developing Health Management Strategies Using Power Constrained Hardware.� PHM Society Conference, 2018, 10(1). https://doi.org/10.1234/phmconf.2018.v10i1.584

3. Babineau, G., Jones, R. and Horowitz, B. �A System-Aware Cyber Security Method for Shipboard Control Systems with a Method Describe to Evaluate Cyber Security Solutions.� IEEE Conference on Technologies for Homeland Security (HST), Waltham, MA, 2012, pp. 99-104. https://ieeexplore.ieee.org/document/6459832

KEYWORDS: Machine Learning; Cybersecurity; Vulnerabilities; Data Analysis; Sensor Network; Cyberattacks; ML; CBM+; CBM; Condition Based Monitoring Plus; Condition Based Maintenance