AI Pattern Recognition of Adversary Spacecraft Operations

As space becomes an increasingly contested domain, identifying and tracking kill chains is critical to the protection and defense of on-orbit satellite systems. A kill chain outlines the sequence of  actions an adversary takes to execute an attack, from initial  reconnaissance to target engagement. Understanding these attack pathways enables early threat detection, rapid response, and mitigation  strategies to prevent or minimize damage to critical space assets. By leveraging advanced threat intelligence, cyber defense mechanisms, and real-time situational awareness, satellite operators can disrupt adversary kill chains before they reach their objectives. This SBIR aims to enhance on our work completed within the SDA Tap Lab where we developed and implemented advance data aggregation, storage and satellite association mapping and explore methodologies for identifying and analyzing kill chains in the space domain, emphasizing their role in proactive defense, system resilience, and mission assurance. Advancements in machine learning (ML) offer powerful tools for detecting and analyzing threats by leveraging publicly available data to map relationships between space-based and ground-based networks. By processing vast amounts of open-source intelligence (OSINT), including telemetry data, radio frequency signals, satellite imagery, and internet traffic patterns—ML algorithms can identify dependencies, uncover hidden vulnerabilities, and which could then be used to predict potential adversary actions. These insights enhance situational awareness, enabling operators to detect and exploit gaps in the kill chain, assess risks, and strengthen defensive measures. As adversaries increasingly exploit publicly accessible data for targeting purposes, integrating ML-driven analytics into satellite security strategies is essential for proactive threat mitigation and ensuring the resilience of space assets. Objective: Develop a system to identify and track kill chains in space, enabling early detection, rapid response, and mitigation of threats to on-orbit satellite systems through comprehensive analysis of adversary actions from reconnaissance to target engagement.Technical Merit: Utilizing machine learning, graph analytics, and behavioral modeling,  the system identifies adversary patterns and tracks the progression of potential attack sequences, enabling early detection of hostile actions  against space assets.Team: SDA TAP Lab research team of three companies led by Planetary Systems AI, a proven enterprise AI/ML software manufacturer, and including Stottler Henke Associates Inc. and Millennial Software Solutions Inc.