SPARTA: Space Attack Tactic Analysis

• The paper introduces SPARTA as a novel framework that evaluates cyber-physical threats by mapping stratified attack vectors across space systems.
• It employs detailed system decomposition and unified kill-chain reconstruction to quantify attack consequences and assess control effectiveness.
• SPARTA integrates MITRE ATT&CK techniques with mission-centric risk scoring to facilitate secure-by-design countermeasure development.

Space Attack Research and Tactic Analysis (SPARTA) is a comprehensive threat-analytic, risk-modeling, and engineering methodology for the systematization, evaluation, and mitigation of cyber and cyber-physical attacks on space infrastructures. Drawing on the stratified tactics, techniques, and procedures (TTP) model inspired by MITRE ATT&CK, SPARTA is tailored to the unique architectural, procedural, and operational dimensions of space systems, spanning the space, link, ground, and user segments. SPARTA has become the canonical framework for rigorous risk-oriented space cybersecurity assessments, enabling detailed kill-chain reconstructions, mission-centric defense engineering, and the principled evaluation of controls effectiveness(Anjum et al., 22 Jul 2025, Ear et al., 2 Dec 2025, Ear, 23 Aug 2025, Yahia et al., 19 Nov 2024).

1. Formal Definition and Taxonomic Structure

SPARTA is formally represented as SPARTA = ⟨T, Tec, SubTec, CM, SC⟩, where:

• Tactics ($T$): The canonical set is {Reconnaissance, Resource Development, Initial Access, Execution, Persistence, Defense Evasion, Lateral Movement, Exfiltration, Impact}, reflecting the full adversarial campaign spectrum observed in space cyber attacks.
• Techniques ($\mathrm{Tec}$): Each tactic τ∈T is mapped to one or more specific techniques, granularly defining attack steps (e.g., “Compromise Hosted Payload,” “Prevent Downlink,” “Trusted Relationship Spoofing”).
• Sub-techniques ($\mathrm{SubTec}$): Relationships between coarse- and fine-grained TTPs, enabling hierarchical analysis.
• Countermeasures (CM): Controls mapped to techniques, such as cryptographic authentication or segmentation.
• Security Controls (SC): Mappings to NIST SP 800-53 or related security standards operationalizing countermeasures(Ear, 23 Aug 2025).

The SPARTA model is complemented by the joint integration of MITRE ATT&CK for enterprise-centric attack surface coverage (ground/user segments), resulting in a combined tactic and technique set (denoted AC_TA, AC_TE) that is leveraged in Unified Space Cyber Kill Chain (USCKC) modeling(Ear et al., 2 Dec 2025).

2. Core Methodologies and Metrics

SPARTA-informed workflows span the entire lifecycle of attack surface mapping, component decomposition, threat identification, risk scoring, and mitigation design:

1. System Model Construction: The target infrastructure is mapped to a graph $G = (V, E)$, with vertices representing functional modules (e.g., TT&C, payload controller, user terminal) and edges representing network or RF/optical links(Ear et al., 2 Dec 2025).
2. Component and Data-Flow Decomposition: Each segment (space, link, ground, user) is decomposed into low-level blocks and inter-block flows, uncovering trust boundaries and data dependencies. Representative data-flow models are provided for both monolithic LEO and networked constellation architectures, employing both textual and LaTeX diagrams for precise documentation(Yahia et al., 19 Nov 2024).
3. Attack Surface Enumeration and Threat Mapping: For each component and flow, SPARTA techniques (e.g., IA-0006, IA-0009, DE-0002) are matched, producing a fine-grained threat surface table. Adversary goals such as command injection, eavesdropping, jamming, and payload-data compromise are traced to concrete system blocks and data flows(Yahia et al., 19 Nov 2024).
4. Metrics:
   • Attack Consequence:

   $$\text{Consequences} = \left(\bigcup_{s \in S_s} \vec{s}^{(s)},\; \bigcup_{g \in G_g} \vec{g}^{(g)},\; \bigcup_{u \in U_u} \vec{u}^{(u)},\; \bigcup_{\ell \in L_\ell} \vec{\ell}^{(\ell)}\right)$$

   Each vector $\vec{x}$ quantifies segment-specific mission/service degradation. - Attack Sophistication: Maximum sophistication weights assigned per tactic and technique, aggregated over all plausible attack chains. - USCKC Likelihood: For chain $KC = \langle te_1, ..., te_n\rangle$, $L(KC) = \min_k L(te_k)$; attack-level likelihood is $\max_i L(KC_i)$ across all chains(Ear et al., 2 Dec 2025). - Risk Scoring: Notional Risk Scores (NRS) via $R = M(\ell, \eta)$ (likelihood, impact), consulting a 5×5 matrix; or via MCDA:

   $$\text{RiskScore} = L W_L + V W_V + A W_A + I W_I + (10 - C) W_C$$

   with segment-specific parameterization(Anjum et al., 22 Jul 2025, Ear, 23 Aug 2025).

3. Attack Vector Taxonomies and Scenario Generation

Comprehensive taxonomies classify threat vectors by segment, mapping attack tactics and techniques to STRIDE categories and kill-chain phases(Anjum et al., 22 Jul 2025):

• Ground: Credential compromise, supply-chain tampering, malware, DDoS.
• Link: Jamming, spoofing (GNSS, control, payload), MitM, eavesdropping.
• Space: Firmware exploits, backdoors, payload tampering, denial via hardware faults.
• Constellations: Lateral movement, crosslink jamming/spoofing, hardware Trojans, and multi-node attacks.

Emergent scenario-based modeling is enabled by the ICARUS matrix, a scenario generator spanning axes: Threat Actors (A), Motivations (B), Methods (C), Victims/Stakeholders (D), Capabilities Affected (E), yielding over $4 \times 10^6$ unique prompts(Lin et al., 17 Jun 2024). This combinatorial approach supports attack-tree enrichment, threat simulation, and red-team defense validation within SPARTA-informed wargaming and risk-assessment platforms.

4. Secure-by-Design Principles and Countermeasures

Adoption of SPARTA in system-engineering workflows directly guides the application of secure-by-design principles, each mapped to specific attack techniques and data flows(Yahia et al., 19 Nov 2024):

• COMSEC (CM0002): Cryptographic authentication/integrity for all link-segment comms, neutralizing trusted relationship spoofing and downlink denial.
• Intrusion Detection & Prevention (CM0032): Onboard, real-time anomaly detection for compromise of hosted payload, trusted relationship violations.
• Segmentation (CM0038): Logical/physical isolation constraining lateral movement post-compromise.
• Least Privilege (CM0039): Rights restriction per process, limiting privilege escalation and payload-tradecraft.
• Robust Fault Management (CM0042): Rapid, autonomous error detection and fail-safe rollback.
• Alternate Communications (CM0070): Multi-path redundancy via RF/FSO switches, ensuring availability under jamming.

Mitigation efficacy is monitored by qualitative and quantitative physical-layer and system-engineering metrics (e.g., BER, SNR, detection latency), and the impact of security-block refactoring is codified in precise “SHALL” requirements directly traceable to SPARTA threats and controls(Yahia et al., 19 Nov 2024).

5. Algorithmic Frameworks: Extrapolating and Hardening Against Attack Chains

Dealing with incident data sparsity, SPARTA employs formal algorithms for (a) reconstructing plausible USCKCs by extrapolating missing TTPs/steps using system graph, tactic/technique space, and narrative partitioning, and (b) mission-centric risk analysis and hardening(Ear et al., 2 Dec 2025, Ear, 23 Aug 2025).

• Extrapolate–USCKC: Given partial incident records, recursively infer plausible tactics and techniques filling gaps between observed steps, generating (and pruning) the set of attack chains consistent with system architecture and known dependencies.
• Mission Risk Analysis & Hardening: For mission-critical flows, compute compromise likelihood by recursive aggregation and cascading propagation, then select minimal control sets to reduce mission risk below thresholds.

Experimental studies on 108 documented space-cyber incidents yield >6000 technique-level USCKCs. Results demonstrate that link-segment attacks (jamming, spoofing) are both high-consequence and frequently preventable via cryptographic, resilient design; low-sophistication attacks account for most real-world impact, underscoring the need to prioritize “low-hanging fruit” controls(Ear et al., 2 Dec 2025, Ear, 23 Aug 2025).

6. Testbeds, Detection, and Evaluation Platforms

Unified Cybersecurity Testing Lab (SAAMD) architectures provide practical, modular, multi-domain evaluation environments for SPARTA-centric research(Costin et al., 2023). Core hardware includes SDRs, drone platforms for mobile attack injection, and protocol-emulation targets (CCSDS, EPIRB/COSPAS, AIS). Attack methodologies supported encompass signal spoofing, replay, jamming, fuzzing, with quantitative measurement of attack success rates, minimal SNR thresholds, and device-specific vulnerabilities.

Integrated SDA–communication systems apply machine learning (CNNs) for attack detection in real-time on-orbit comms hardware, achieving ≥97.8% detection over 12 randomized attack configurations(Cetin et al., 2022). Key detection outputs (e.g., confusion matrices, false alarm rates, SNR sensitivity) directly inform SPARTA’s tactic discrimination and countermeasure automation pipelines.

7. Research Challenges, Limitations, and Future Directions

Multiple open challenges remain within the SPARTA research program(Anjum et al., 22 Jul 2025, Ear, 23 Aug 2025):

• Early-life-cycle Security Integration: Embedding risk modeling and controls selection during early mission design.
• Efficient Control Selection and Dynamic Response: Lightweight, autonomous countermeasures suitable for constrained, heterogeneous space platforms.
• AI/ML-Empowered Threat Detection: Adversarial robustness, transfer learning, federated detection across satellite constellations.
• Supply Chain Traceability: At-scale COTS provenance, hardware Trojan screening.
• Uncertainty Quantification and Model Validation: Empirical validation of risk and consequence metrics, automated extraction from CTI, uncertainty-aware mission risk scoring.
• Expansion of Taxonomic Reach: Scenario generation covering AI/quantum comms, user-segment social engineering, and environmental hazards.
• Standardization and Regulatory Harmonization: Integration of space-specific best practices with evolving terrestrial standards (CCSDS, ISO, NIST, SP 800-160).

The SPARTA framework, with its empirically validated models, comprehensive taxonomies, and actionable system design principles, is established as the reference architecture for rigorous assessment, mitigation, and simulation of space cyber risk(Yahia et al., 19 Nov 2024, Ear, 23 Aug 2025, Ear et al., 2 Dec 2025, Anjum et al., 22 Jul 2025, Costin et al., 2023, Cetin et al., 2022, Lin et al., 17 Jun 2024).