Current SecureAmerica Institute projects as part of a nationwide initiative to empower a secure and resilient U.S. manufacturing and defense industrial base.
Vital industrial bases no longer have any domestic presence creating a gap in an American ability to remain resilient during potential disruptions. Deep View will conduct a multi-tier supply chain and industrial base assessment to evaluate the technology readiness levels of interconnection security within the selected manufacturing industrial base. Project findings will be incorporated into SAI’s long-term engagement plan with industry members and raise industrial base and supply chain analysis where collective influence of SAI members can inform industry-wide risk mitigation efforts.
Emerging needs and vulnerabilities in the manufacturing and defense industrial base supply chain cannot be identified without evaluating supply chain participants’ current digital maturity level across the six digitization pillars of digital development. This project will survey partners to determine the readiness of supply chain participants for satisfying the six pillars (digital development, synchronized planning, intelligent supply, smart factory, dynamic fulfillment, connected customer) to analyze and prepare a digital supply chain framework for participants.
Increase in automation, specifically industrial robots, has created new access points for cybersecurity vulnerabilities in industrial internet of things (IIoT) components. This project will develop an external system consisting of sensing and human operator observations to predict when loss of trust will occur in robotic systems and identify behavior that may indicate a cyber intrusion.
Cybersecurity by system design considerations are needed to enhance the testing and assure the built-in cybersecurity in emerging supply management chains. This project will build a global, scalable, crowdsourcing tool to help certify cybersecurity postures for all aspects of supply management systems.
Additive manufacturing platforms are particularly vulnerable to cyber-attack since occurred attacks easily be mistaken as process or part defects due to inherent process variability. This project will focus on the development of machine learning based algorithms and tools to enable cyber-physical manufacturing systems to differentiate between faulty and compromised products.
Due to growing adoption of IIoT devices and edge computing, currently employed security solutions are insufficient to thwart strong adversaries from stealing and manipulating sensitive data. This project will develop a new trusted execution environment to enable end-to-end data protection and prevent theft of sensitive data.
Few tools allow for the intuitive creation of sensor models which can then be tested across any number of scenario configurations. This project will explore an approach to leverage existing advanced sensor fusion modeling with an easy to use graphical user interface helping designers, operators, and practitioners to analyze and predict sensor-fusion based scenarios.
Improvements in manufacturing agility and flexibility through automation are needed to ensure security and resilience in information technology and operational technology networks. This project will develop solutions to achieve resilience by resisting attacks, detecting attacks in progress, and ensuring an IT/OT system can automatically restore itself to a trusted state and continue operation.
Direct Laser Metal Sintering (DMLS) is a powerful manufacturing approach to creating high-performing propulsion systems for hypersonics, but is limited by the speed a component may be produced. This project will create a sensitivity analysis of a set of approaches to increase the security of additive manufacturing supply chains by improving the yield and throughput speed.