SBIR Phase I: Performant Distributed Ledgers for Cybersecurity Applications

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to advance a novel, distributed ledger-based technology to improve Application-Program Interfaces (APIs) security for enterprises. APIs are the way that machines communicate with each other, where these machines may be servers, docker containers, IoT devices, sensors, and more.

Enterprises are moving to using hybrid cloud deployments, mixing public cloud with private and edge networks. Further, enterprises are deploying a wide range of open architectures that support mobile access, automated deployment workflows, and application architectures like microservices, IoT enclaves, and sensor networks. Increasingly as an enterprise turns to APIs to stitch these diverse network elements together, API security is a critical element of any enterprise’s cybersecurity strategy.

The company’s proposed API security platform leverages a private and permissioned distributed ledger network (DLN) at the heart of its product. In fact, this DLN enables provision of the first implementation of multi-factor authentication (MFA) for APIs. To meet the scale and latency requirements of API authentication in real-time without sacrificing security guarantees, the proposed DLN must be highly performant and efficient in both storage and processing.

This SBIR-funded research seeks to advance DLN technology but is also more broadly applicable to the advancement of enterprise applications of distributed ledgers that would benefit from pruning, flexible scaling and orchestration, and heightened performance.

This SBIR Phase I project proposes to advance this foundational blockchain framework by discovering and evaluating approaches to elevate performance of the DLN in the scope of authentication application. The project will measure and compare the effectiveness of potential advancements to the DLN platform, including: 1) Horizontal scaling of the blockchain network, i.e. varying the number of peers or other components in the network, 2).

Pruning of the blockchain to only keep relevant and necessary portions of ledger data, i.e. leading to a more efficient ledger access without sacrificing deep verification. The research work will extend modeling and simulation infrastructure to pattern and measure real-world, at-scale scenarios. The research proposed here will have a broad impact to DLT research and serve as a blueprint for production-grade ledger-based technologies to help enable use-cases where the barrier to entry is cost, performance, and/or scalability.

The company will utilize these research findings to develop a production-grade model for a performant, scalable DLT-based security product that can meet scenarios as expansive as high-performance cloud computing, and as granular as embedded, industrial control systems and sensor networks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.