In spacecraft architectures containing aggregator or relay nodes, observable computation and communication patterns may reveal valid aggregation cycles and traffic-flow relationships involving critical nodes, root nodes, or ground termination points. This countermeasure is applicable when the threat model identifies a credible adversary capability to observe node power or electromagnetic activity and correlate those observations with RF transmission activity. While camouflaging all network traffic through constant high-power transmission is energy-prohibitive, selectively obscuring aggregator node behavior through dummy process execution provides a practical alternative. This countermeasure requires aggregator nodes to execute dummy workloads whose observable characteristics are sufficiently similar to genuine aggregation cycles to make reliable classification difficult within the mission-defined adversary model. Evaluation should consider applicable power, electromagnetic, execution-duration, processor-activity, memory-access, and RF-correlated features rather than matching only an average power-consumption curve. Two properties are essential for effectiveness: first, dummy processes must vary in their execution pattern, using a different dummy process each time or maintaining a low repetition rate, to prevent adversaries from identifying a distinguishable signature that differentiates dummy from genuine execution; second, timing of dummy execution must be carefully controlled, with a dummy process executed every time the aggregator receives a transmission and randomly during idle periods, to prevent adversaries from correlating the presence or absence of radio frequency (RF) transmissions with power consumption curves to identify and discard dummy activity. Together, these properties are intended to reduce an observer’s ability to distinguish valid aggregation cycles and infer traffic flow toward a critical root or base-station node. They do not guarantee that aggregator nodes, network topology, or the base station cannot be identified through other observations.
Acquisition requirements should address dummy process execution as a traffic flow obfuscation capability for aggregator nodes in spacecraft sensor networks or constellation architectures where base station identification represents a significant operational security risk. Requirements should define the observable features and adversary capabilities against which genuine and dummy aggregation activity must be difficult to distinguish. The design must incorporate sufficiently variable dummy execution and the required combination of transmission-correlated and idle-period execution so that the presence or absence of RF activity does not provide a reliable means of discarding dummy observations. Contract language should require that the dummy process design be documented as a controlled deliverable, including the method for generating varied dummy processes, the statistical properties of the idle-period random execution schedule, and the power consumption fidelity of the dummy process relative to genuine aggregation cycles. Evaluation criteria should assess offerors' proposed dummy process architecture, their analysis of the power profile fidelity between dummy and genuine operations, and their approach to validating that the implementation prevents traffic flow identification under realistic adversary observation conditions. Verification should include power trace analysis comparing dummy and genuine execution profiles, and adversarial simulation exercises that attempt to identify aggregation cycles and base station location under the implemented scheme.
Pre-Operations Developer/Supplier
Dummy process design must begin with a characterization of the genuine aggregation cycle power consumption profile, establishing the target power curve that dummy processes must replicate with sufficient fidelity to defeat statistical discrimination by an adversary collecting power traces over extended periods. Dummy execution must provide sufficient variation to avoid a repetitive signature while remaining statistically consistent with genuine aggregation activity. Variation may use randomized execution paths, workload parameters, or selection among validated templates, but variable duration or computation must not introduce features that make dummy activity easier to classify. Repetition rate and distinguishability must be evaluated over the observation duration and measurement capability defined by the threat model. Timing control logic must implement the approved transmission-correlated and idle-period dummy schedule, including dummy execution for each applicable received-transmission event required by the design. Scheduling must preserve safety- and mission-critical timing, power, and thermal limits and must address whether an adversary could generate transmissions that force excessive dummy execution. Unpredictable idle-period scheduling must use an approved random or pseudorandom mechanism with properties appropriate to the threat model. Power budget analysis must account for the sustained overhead of dummy process execution across all operational modes, as the requirement to execute dummy processes upon every received transmission and randomly during idle periods represents a non-trivial continuous power cost that must be within the aggregator node's energy margin. The fidelity of the dummy power profile relative to genuine aggregation should be validated through hardware-level power measurement rather than simulation alone, as hardware-level power consumption behavior may deviate from model predictions in ways that inadvertently create discriminating signatures.
Sustainment & Maintenance Government
Random or pseudorandom state used for dummy scheduling must be securely initialized, protected against unauthorized observation or modification, and managed according to the approved generator design. Reseeding or state refresh must occur when required by the generator, mission duration, compromise assumptions, or observed repetition risk. Effectiveness should be evaluated through direct analysis of schedule predictability and repetition rather than assuming that arbitrary periodic seed replacement improves security. Power consumption monitoring of aggregator nodes should include tracking of the overall power profile across transmission and idle periods, with significant deviations from the expected profile investigated as potential indicators that dummy process execution has been interrupted or is no longer replicating the genuine aggregation profile with required fidelity. Any software update to aggregator node firmware that affects the dummy process generation logic, the transmission-triggered execution handler, or the idle-period scheduling mechanism must be validated through power trace measurement before deployment, confirming that the update preserves the statistical indistinguishability of dummy and genuine operations. Changes to network topology, routing, node population, aggregation workload, communications protocol, firmware, processor scheduling, or power-management configuration must be assessed for their effect on the observable characteristics of genuine and dummy aggregation activity. Changes that materially alter those characteristics require recalibration and representative side-channel regression testing before the updated configuration is relied upon.
Sustainment & Maintenance Developer/Supplier
Random or pseudorandom state used for dummy scheduling must be securely initialized, protected against unauthorized observation or modification, and managed according to the approved generator design. Reseeding or state refresh must occur when required by the generator, mission duration, compromise assumptions, or observed repetition risk. Effectiveness should be evaluated through direct analysis of schedule predictability and repetition rather than assuming that arbitrary periodic seed replacement improves security. Power consumption monitoring of aggregator nodes should include tracking of the overall power profile across transmission and idle periods, with significant deviations from the expected profile investigated as potential indicators that dummy process execution has been interrupted or is no longer replicating the genuine aggregation profile with required fidelity. Any software update to aggregator node firmware that affects the dummy process generation logic, the transmission-triggered execution handler, or the idle-period scheduling mechanism must be validated through power trace measurement before deployment, confirming that the update preserves the statistical indistinguishability of dummy and genuine operations. Changes to network topology, routing, node population, aggregation workload, communications protocol, firmware, processor scheduling, or power-management configuration must be assessed for their effect on the observable characteristics of genuine and dummy aggregation activity. Changes that materially alter those characteristics require recalibration and representative side-channel regression testing before the updated configuration is relied upon.
Information is extracted not by reading files or decrypting frames but by observing physical or protocol byproducts of computation, power draw, electromagnetic emissions, timing, thermal signatures, or traffic patterns. Repeated measurements create distinctive fingerprints correlated with internal states (key use, table loads, parser branches, buffer occupancy). Matching those fingerprints to models or templates yields sensitive facts without direct access to the protected data. In space systems, vantage points span proximity assets (for EM/thermal), ground testing and ATLO (for direct probing), compromised on-board modules that can sample rails or sensors, and remote observation of link-layer timing behaviors.
In a terrestrial environment, threat actors use traffic analysis attacks to analyze traffic flow to gather topological information. This traffic flow can divulge information about critical nodes, such as the aggregator node in a sensor network. In the space environment, specifically with relays and constellations, traffic analysis can be used to understand the energy capacity of spacecraft node and the fact that the transceiver component of a spacecraft node consumes the most power. The spacecraft nodes in a constellation network limit the use of the transceiver to transmit or receive information either at a regulated time interval or only when an event has been detected. This generally results in an architecture comprising some aggregator spacecraft nodes within a constellation network. These spacecraft aggregator nodes are the sensor nodes whose primary purpose is to relay transmissions from nodes toward the ground station in an efficient manner, instead of monitoring events like a normal node. The added functionality of acting as a hub for information gathering and preprocessing before relaying makes aggregator nodes an attractive target to side channel attacks. A possible side channel attack could be as simple as monitoring the occurrences and duration of computing activities at an aggregator node. If a node is frequently in active states (instead of idle states), there is high probability that the node is an aggregator node and also there is a high probability that the communication with the node is valid. Such leakage of information is highly undesirable because the leaked information could be strategically used by threat actors in the accumulation phase of an attack.
The [spacecraft] shall protect system components, associated data communications, and communication buses in accordance with: (i) national emissions and TEMPEST policies and procedures, and (ii) the security category or sensitivity of the transmitted information, and shall demonstrate compliance via pre‑launch TEMPEST‑like evaluation for co‑located payload configurations.{SV-CF-2,SV-MA-2}{PE-14,PE-19,PE-19(1),RA-5(4),SA-8(18),SA-8(19),SC-8(1)}
The measures taken to protect against compromising emanations must be in accordance with DODD S-5200.19, or superseding requirements. The concerns addressed by this control during operation are emanations leakage between multiple payloads within a single space platform, and between payloads and the bus.
SPR-38
The [spacecraft] shall be designed so that it protects itself from information leakage due to electromagnetic signals emanations.{SV-CF-2,SV-MA-2}{PE-19,PE-19(1),RA-5(4),SA-8(19)}
This requirement applies if system components are being designed to address EMSEC and the measures taken to protect against compromising emanations must be in accordance with DODD S-5200.19, or superseding requirements.
SPR-115
The [organization] shall describe (a) the separation between RED and BLACK cables, (b) the filtering on RED power lines, (c) the grounding criteria for the RED safety grounds, (d) and the approach for dielectric separators on any potential fortuitous conductors, and shall provide quantitative separation distances, filter specifications, grounding resistance criteria, and dielectric separator material properties.{SV-CF-2,SV-MA-2}{PE-19,PE-19(1)}
Physical separation of classified (RED) and unclassified (BLACK) signal paths prevents compromising emanations. Defined separation distances, filtering, and grounding reduce leakage risk. Quantitative criteria ensure repeatable and verifiable implementation. This protects against unintended signal coupling and data leakage.