SBIR Phase I: A Cyber Assured Space Internet Device

SBIR 第一阶段：网络安全空间互联网设备

• 批准号: 2327618
• 负责人: Kevin Jackson
• 金额: $ 27.5万
• 依托单位: Forward Edge AI, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327618&HistoricalAwards=false
• 关键词: SBIR; Phase; Cyber; Assured; Space

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project presents urgently needed improvements to cybersecurity in space to enable a larger scale, higher throughput, and a more securely interconnected ecosystem. This includes enabling high-throughput on-orbit manufacturing for the next ten years and is aimed squarely at small and medium manufacturers (SMM), large manufacturers, and original equipment manufacturers (OEM) that will supply large-scale space production industries. The ability to use Artificial Intelligence/Machine Learning (AI/ML) to remotely modify, optimize, and enhance the resiliency of Cube Satellites to cyberattacks is crucial to this evolving industry. The solution extends to the high growth commercial space industry-related Earth Observation (EO), and Direct Satellite to Device/Smartphone markets. The development of small satellites has notably increased the interest of private companies and government agencies in investing in this field, as it allows for more affordable access to new business models in space, including satellite constellations. Space applications, ranging from machine to machine (M2M), the Internet of Things, and Earth observation use cases, are expected to reach more than $22 billion in service revenue by 2031. The market is rapidly moving from an infrastructure-heavy investment cycle towards an as-a-service-focused recurring revenue business model.This SBIR Phase I project will develop the technology needed to accelerate the commercial development of the hybrid space and terrestrial communications architectures, in-space manufacturing, and industrial infrastructure. ML algorithms that can differentiate between anomalies triggered by natural phenomena and cyber-attacks represent a significant advancement. This can be applied at increasingly larger scales, higher-throughputs, and speeds for robust security and acceleration of this sector. Through adaptability and precision, ML can significantly reduce the occurrence of false alarms but also excel at predicting the source of the anomaly and attributing the anomaly to its origin. Applying the ML predictive capabilities enhances early warning systems, fortifies cybersecurity measures, and ensures continuous monitoring in an ever-evolving threat landscape. The project would accelerate the integration of terrestrial telecommunications networks and satellite communications technologies, decrease costs, increase service coverage, and provide added resilience and multi-level security compatibility to the nation’s communication infrastructure. Mimicking the operational capabilities of the human immune system will allow for the long-term and evolving effectiveness of a space platform's cyberattack detection and response capabilities. This decentralized approach will be able to leverage decentralized autonomous organizations and strategic defense capabilities to accelerate human endeavors in space.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.

该小型企业创新研究（SBIR）第一阶段项目具有更广泛的影响/商业潜力，迫切需要太空网络安全，以实现更大规模、更高吞吐量和更安全地互连的生态系统改进，其中包括实现高吞吐量。未来十年的轨道制造，主要针对中小型制造商（SMM）、大型制造商和原始设备制造商（OEM），为大规模太空生产行业提供使用人工智能/机器学习的能力。 (AI/ML) 远程修改、优化和增强立方体卫星对网络攻击的弹性对于这个不断发展的行业至关重要，该解决方案扩展到与高速增长的商业航天行业相关的地球观测 (EO) 和直接卫星到设备。 /智能手机市场。小型卫星的发展显着增加了私营公司和政府机构在这一领域的投资兴趣，因为它允许更经济地获得新的太空商业模式，包括卫星星座的空间应用。机器对机器 (M2M)、物联网和地球观测用例的服务收入预计到 2031 年将达到超过 220 亿美元。市场正在迅速从基础设施密集的投资周期转向基础设施投资周期。以服务为中心的经常性收入商业模式。该 SBIR 第一阶段项目将开发加速混合空间和地面通信架构、空间制造和能够区分异常的工业机器学习算法的商业开发所需的技术。由自然现象和网络攻击引发的数据代表了一项重大进步，它可以以越来越大的规模、更高的吞吐量和速度应用，以实现该领域的强大安全性和加速性。通过适应性和精确性，机器学习可以显着减少错误的发生。该项目不仅能够发出警报，而且还擅长预测异常的来源并将异常归因于其根源。加速地面电信网络和卫星通信技术的整合，降低成本，扩大服务覆盖范围，并为国家通信基础设施提供额外的弹性和多层次的安全兼容性，从而实现长期的通信能力。这种去中心化的方法将能够利用去中心化的自治组织和战略防御能力来加速人类在太空的努力。该奖项反映了美国国家科学基金会的法定使命，并被视为是。值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。