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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力表明，迫切需要改进空间网络安全，以实现更大规模、更高吞吐量和更安全的互联生态系统。这包括在未来十年实现高通量在轨制造，并直接针对中小型制造商（SMM），大型制造商和原始设备制造商（OEM），这些制造商将提供大规模的空间生产行业。使用人工智能/机器学习（AI/ML）远程修改、优化和增强Cube卫星抵御网络攻击的能力对这个不断发展的行业至关重要。该解决方案扩展到与地球观测（EO）相关的高增长商业航天工业，以及直接卫星到设备/智能手机市场。小型卫星的发展大大增加了私营公司和政府机构对这一领域投资的兴趣，因为它使人们能够以更低的价格获得包括卫星星座在内的太空新商业模式。到2031年，包括机器对机器（M2M）、物联网和地球观测用例在内的空间应用预计将达到220亿美元以上的服务收入。市场正迅速从以基础设施为主的投资周期转向以服务为中心的经常性收入商业模式。SBIR第一阶段项目将开发加速空间和地面混合通信架构、空间制造和工业基础设施商业开发所需的技术。机器学习算法可以区分由自然现象和网络攻击引发的异常，这是一项重大进步。这可以应用于越来越大的规模、更高的吞吐量和速度，以实现该部门的强大安全性和加速。通过适应性和精确性，机器学习可以显著减少假警报的发生，同时也擅长预测异常的来源并将异常归因于其起源。应用机器学习预测功能增强了早期预警系统，加强了网络安全措施，并确保在不断变化的威胁环境中进行持续监控。该项目将加速地面通信网络和卫星通信技术的整合，降低成本，增加服务覆盖范围，并为国家通信基础设施提供额外的弹性和多层次安全兼容性。模仿人体免疫系统的作战能力将使空间平台的网络攻击探测和响应能力具有长期和不断发展的有效性。这种分散的方法将能够利用分散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，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。