`spoof-detect` (scenario kind)

Scenario kind of the engine v0.38.0, family Jamming, spoofing and the spectrum. Advisory: analysis output from a research and engineering tool, not type-approved navigation equipment and not a safety-of-life service. Every label here is the engine's or the verification matrix's; a kind is MODELLED unless the matrix says otherwise (how a label is earned: the README's Evidence section).

What it does#

Combined RF/measurement spoof detector (multi-SV RAIM-consistency + AGC + SQM, fused) vs a parameterised attack (power advantage, carrier-phase alignment, time/position push; TEXBAT-style).

Inputs#

From the schema (kshana kinds --json):

Required fields: attack
Optional fields: satellites, detector

The example scenarios/spoof-detect.toml sets, among others:

Field	Value	Unit
`attack.carrier_aligned`	false	flag
`attack.el_imbalance`	0.12	1
`attack.num_biased`	3	count
`attack.power_advantage_db`	1.5	dB
`attack.push`	position	text
`attack.push_magnitude_m`	75	m
`detector.agc_margin_db`	3	dB
`detector.fusion_threshold`	0.5	not stated
`detector.raim_p_fa`	0.001	not stated
`detector.sat_power_dbm`	-130	dBm
`detector.sigma_m`	5	m
`detector.sqm_tolerance`	0.1	not stated

Run it#

kshana scenarios/spoof-detect.toml

2 bundled scenarios use this kind; the others are in scenarios/.

What it gives#

The scalar results of that run, with the units the engine states:

Result	Value	Unit
`attack.carrier_aligned`	false	flag
`attack.el_imbalance`	0.12	1
`attack.num_biased`	3	count
`attack.power_advantage_db`	1.5	dB
`attack.push`	position	text
`attack.push_magnitude_m`	75	m
`decision.agc_excess_db`	1.5	dB
`decision.fused.alert`	true	flag
`decision.fused.layers.agc`	false	flag
`decision.fused.layers.raim`	true	flag

The chart the engine writes for scenarios/spoof-detect.toml, kind spoof-detect

Evidence#

MODELLED: the verification matrix rows that name this kind.

MODELLED Clock-aided χ², RAIM, AGC, SQM fused per-epoch security FoM Tests: spoof_texbat_validation.rs.

The rows the run itself exercised, with the oracle the matrix gives each (the public sources are named there):

MODELLED Clock-aided χ², RAIM, AGC, SQM fused per-epoch security FoM. Oracle (ExternalDataset): TEXBAT scenario parameters (Humphreys 2012) — characterisation, not pinned vectors. 0.30 external comparison, a finding (stays MODELLED): on JammerTest 2024 (Bleik; GPL-3.0 data) the observable-level monitors (clock-aided chi-square, RAIM, solve failure) raised no alarm within 10 s of any of the 8 evaluable published spoofing onsets (where the pre-onset window was clean, the first alarm came 10.2 to 39 s after the pu
MODELLED Deterministic, scenario-hashed, SBOM + cross-platform golden gates. Oracle (ExternalDataset): SBOM conformance to the official CycloneDX 1.5 JSON Schema (+ valid SPDX identifiers) — an external published standard, zero validation errors over the full dependency graph; the FoM-determinism / byte-reproducibility part remains a pinned self-consistency check, so the row stays MODELLED
PARTNER Not provided — Kshana models signal performance, not payload/antenna hardware. Oracle (NoneKind): none: a partner-owned discipline, with no module and no test by design

Limitations#

What the run's own report says is not modelled or not validated:

Spoofing detection is MODELLED, not validated: a sub-claim is externally checked, but the whole capability composes modelled pieces, so the capability stays Modelled. (verification matrix (docs/MODELLED-RATIONALE.md))
Reproducibility & software assurance is MODELLED, not validated: a sub-claim is externally checked, but the whole capability composes modelled pieces, so the capability stays Modelled. (verification matrix (docs/MODELLED-RATIONALE.md))

spoof-detect (scenario kind)